32° Simposio Internacional de estadística 2023 : bioestadística y datos funcionales

Vista sencilla de ítem
dc.contributor.corporatenameUniversidad Nacional de Colombia. Sede Bogotá. Facultad de Ciencias. Departamento de Estadísticaspa
dc.date.accessioned2025-02-21T20:26:54Z
dc.date.available2025-02-21T20:26:54Z
dc.date.issued2023
dc.descriptionilustraciones, diagramas, mapas, tablasspa
dc.description.abstractEl Simposio de Estadística de la Universidad Nacional de Colombia nace en 1990 como un encuentro que tenía como tema central el análisis de regresión y con el pasar de los años, diversas áreas de investigación estadística, tanto teórica como aplicada y temas afines fueron integrándose de manera paulatina, convirtiendo al Simposio en un espacio de interacción, dialogo, discusión, actualización, revisión y divulgación de los tópicos y saberes que se encuentran en tendencia, haciendo de este evento, un referente a nivel nacional y regional. De esta forma, el departamento de Estadística de la Universidad Nacional ha dispuesto desde el inicio, un comité organizador, el cual, cada año ha propuesto uno o varios temas de interés y ha invitado a varios expertos nacionales e internacionales, que han impartido cursillos y conferencias y socializado sus conocimientos con estudiantes, profesores e investigadores de distintas áreas del saber. Es importante destacar que, a lo largo de estos 30 años, el simposio se ha realizado en distintas ciudades y municipios de Colombia, como lo son: Bogotá, Santa Marta, Cartagena, Rionegro, San Andrés, Armenia, Paipa, Bucaramanga, Cali, Medellín, Sincelejo y Barranquilla, realizándose en algunas versiones en conjunto con otras instituciones y convirtiéndose en 2012, en un evento de carácter internacional. Con perseverancia, dedicación, calidad y trabajo, hemos llegado en el año 2023, a la versión número 32 de este evento en la ciudad de Ibagué, Tolima. Los temas que se convocaron este año fueron Bioestadística y Datos Funcionales, motivados principalmente por ser temas de reciente y marcada importancia que se da en un contexto global, regional y nacional en la que los datos cada vez tienen mayor volumen y que las herramientas que se necesitan para su análisis tienen un fuerte y relevante sustento estadístico, con un creciente interés por parte de la comunidad estadística nacional. Agradecemos a cada uno de los participantes por ser parte de este evento, esto muestra la acogida que cada año tiene este simposio a lo largo y ancho del territorio nacional, en distintas instituciones académicas y de investigación, mostrando los distintos resultados y perspectivas que ofrecen cada una de las ramas de la estadística en la solución de problemas de interés local, regional, nacional e internacional y como se hace necesaria la comprensión de las temáticas propuestas y la continuidad de la realización de nuestro Simposio. Gracias a estos encuentros se ha llegado a tener un fortalecimiento dinámico y sostenido en el quehacer estadístico nacional, con lo cual los pioneros de esta idea pueden sentirse satisfechos, pues los diferentes comités organizadores le han cumplido al país y a la Universidad con el objetivo propuesto en la primera versión. Les esperamos en el 33 Simposio Internacional de Estadística 2024 (Texto tomado de la fuente)spa
dc.description.edition32 edición
dc.description.tableofcontentsSIMULATION, ESTIMATION, AND GOODNESS FIT FOK THE STOCHASTIC BRIDGE -- CARTA DE CONTROL MULTIVARIADA SIN DISTRIBUCION PARA DATOS HIBRIDOS FUNCIONALES Y VECTORIALES -- ESTUDIO EXPLORATORIO PARA DETERMINAR LA CANTIDAD DE REZAGOS SIGNIE CATIVOS EN SERIES DE TIEMPO MEDIANTE EL USO DE DIFERENTES COEFICIENTES -- OPINIONES EN TWITTER SOBRE LA DEFORESTACIÓN EN ELAMAZONAS DEL 2010 AL 2022: UN ANALISIS DE SENTIMIENTOS CON REDES NEURONALES -- APLICACION SHINY - INTERVALOS DE VEROSIMILITUD -- APLICACIÓN SHINY - PERFILES DE VEROSIMILITUD -- MDA (MONOTONE DATA AUGMENTATION) APLICADO A DATOS DE DESNU- TRICIÓN AGUDA EN MENORES DE 5 AÑOS EN LA CIUDAD DE MEDELLIN -- MODELOS GARCH MULTIVARIADOS PARA EL CALCULO DE VALOR EN RIESGO DE ACTIVOS FINANCIEROS UTILIZANDO RY PYTHON -- ECUACTONES ESTRUCTURALES: UNA HERRAMIENTA PODEROSA PARA ANALIZAR LA SALUD MENTAL EN ESTUDIANTES DE BASICA SECUNDARIA Y MEDIA -- DATOS DE GEORREFERENCIACIÓN GPS EN EL COMPORTAMIENTO DE BOVINOS -- MODELO ESTADISTICO ESPACIAL USANDO EL ENFOQUE BAYESIANO SPDE ALIMENTADO DE UNA RED NEURONAL ARTIFICIAL PARA EL ANALISIS DELA RENTA URBANA -- AN OPTIMAL RUNS TEST FOR ONE SAMPLE LOCATION PROBLEM -- ANALISIS DE LA CRIMINALIDAD EN COLOMBIA: IDENTIFICACION DE PA- TRONES Y TENDENCIAS EN LA FRECUENCIA DE VICTIMAS -- CONEIDENCE SETS BOOTSTRAP ON CONTEXT OF TOPOLOGICAL DATA ANALYSIS -- A UMAP TECHNIQUE APPLICATION TO ANALYZE TRAFFIC IMAGES FROM GOOGE MAPS -- APLICACION DEL MODELO FAY-HERRIOT EN LA GRAN ENCUESTA INTEGRADA DE HOGARES (GEIH) DICIEMBRE 2022 -- ANALISIS DE DIVERSOS MODELOS GEOESTADISTICOS DE DISTRIBUCION ESPACIAL DE LA PRECIPITACIÓN -- CARACTERIZACIÓN DEL DISCURSO DE POSESION PRESIDENCIAL E IDENTIFICACIÓN DE COMUNIDADES POLITICAS EN COLOMBIA: APROXIMACION EMPIRICA DESDE EL ANALISIS DE REDES SOCIALES -- CONDICIONES DE CALIDAD DE VIDA DE SAN ANDRES ISLA: UNA BREVE DESCRIPCIÓN MULTIVARIADA -- MODELAMIENTO DE LA CALIDAD DEL AIRE EN LA CIUDAD DE BOGOTA UTILIZANDO CADENAS DE MARKOV -- COMPARACIÓN DEL USO DE HERRAMIENTAS TECNOLÓGICAS PARA EL APRENDIZAJE SIGNIFICATIVO DE LAS MATEMÁTICAS BÁSICAS EN PREGRADO, MEDIANTE MODELOS DE ECUACTONES ESTRUCTURALES LINEALES -- ANALISIS ESPACIAL DE LA PUDRICION BASAL DEL ESTIPITE EN PALMA DE ACEITE CAUSADA POK GANODERMA ZONATUM EN LA ZONA NORTE COLOMBIANA -- DISTRIBUCIÓN ESPACIAL DE LAS POBLACIONES DE L. GIBBICARINA EN UN LOEDE PALMA ACEITE -- EVALUACIÓN COMPARATIVA DELA EFECTIVIDAD DE DOS MÉTODOS RESTAURADORES EN EL MANEJO DE CARIES EN NIÑOS DE ESCASOS RECURSOS EN CALIFORNIA - EE.UU -- MODELOS CONJUGADOS DISCRETOS; UNA APLICACION SHINY PARA LA ENSENANZA DE ESTADISTICA BAYESIANA -- MODELIZACION, ANALISIS Y ESTIMACION EN UN MODELO EPIDEMIOLOGICO CON PERTURBACIONES ALEATORIAS: UNA APLICACIÓN A LAS INFEC- CIONES RESPITARORIAS AGUDAS EN BOGOTA -- EXPOSING RACIAL DISPARITIES OF AL-BASED IMAGING CLASSIFIERS FOR PIGMENTED SKIN LESIONS DAGNOSIS -- ANALISIS DESCRIPTIVO INTERSECCIONAL DEL MERCADO LABORAL EN LA RURALIDAD COLOMBIANA -- COMPORTAMIENTO FISIOLÓGICO DE LA CURUBA UTILIZANDO ANALISIS LONGITUDINAL BASADO EN DISTANCIAS -- COMPARACIÓN DE MODELOS DE DATOS FUNCIONALES Y REDES NEU- RONALES PARA LA CLASIFICACIÓN DE IMÁGENES DE DIAGNÓSTICO DE TUMORES CEREBRALES -- UNA MANERA DE OBTENEK DISTR BUCIONES DE PROBABILIDAD BIVARIADA A PARTIR DE UN CIERTO TIPO DE SUBCOPULA BIVARIADA -- ANALISIS DESCRIPTIVO MULTIVARIADO DE LA SITUACIÓN LABORAL DE LA MUJER EN LA URBANIDAD COLOMBIANA ENFOCADO ENLA POBLACIÓN CON DISCAPACIDAD -- ESTIMACIÓN DE NITRÓGENO FOLIAR EN PALMA DE ACEITE ADULTA POR ESPECTRORRADIOMETRIA: REGRESION ESCALAR- FUNCIONAL EMPLEANDO MINIMOS CUADRADOS FUNCIONALES -- DESAFIOS Y OPORTUNIDADES EN LA COMPRENSIÓN DE LAS DINAMICAS UNIVERSITARIAS EN COLOMBIA: UN ENFOQUE ESTADISTICO -- MODELADO DE CUANTILES EN UN ENTORNO DE REGRESIÓN CON RESPUESTA POSITIVA ASIMÉTRICA Y SU APLICACIÓN EN LA CONSTRUCCIÓN DE CURVAS DE CRECIMIENTO -- ANALISIS DE COMPONENTES PRINCIPALES (ACP) PARA RANQUEAR PAISES CON RESPECTO A SU CALIDAD DE VIDA -- MEDICIÓN DE RIESGO DE INCUMPLIMIENTO DE CONTRATOS DE CAFE EN EL MODELAMIENTO DE ASIGNACION DE CREDITOS -- CROSSCARRY: ANRPACKAGE FOR THE ANALYSIS OF DATA FROMA CROSSOVER DESIGN WITH GEE -- COMPARACION DE UN ENFOQUE BAYESIANO Y DE MACHINE LEARNING PARA LA ESTIMACIÓN DE MEDIDAS CRANEOFACIALES EN NIÑOS -- ENHANCED CONTROL CHARTFORTHE MONITORING OESKEWED-NORMAL SAMPLES -- TAMAÑO DE MUESTRA EN ENSAYOS CLÍNICOS ALEATORIZADOS EN PARALELO CON AJUSTE POR ANALISIS INTERINOS -- CARACTERIZACION DE LAS MATRICES ESTOCASTICAS PARA LA EXISTENCIA DE RAICES MATRICIALES -- A PHASE I FUNCTIONAL CONTROL CHART BASED ON MODIFIED EP- GRAPH INDEX AND MODIFIED BAND DETH -- NUEVAS DISTRIBUCIONES ASIMETRICAS QUE PERMITEN MODELAR CUANTILES MARGINALES EN DATOS POSTTIVOS MUCTIVARIADOS -- ESTIMACION DE PERCENTILES APOYADO EN EL PAQUETE PROBSAMPLINGI DEL SOFTWARE R-PROJECT -- R VS PYTHON VS EXCEL PARA SEMILLEROS. UN EJEMPLO DE SUAVIZAMIENTO EXPONENCIAL Y MEDIAS MÓVILES CON LA OCUPACIÓN DE CAMAS UCI EN BOGOTÁ DURANTE LA EMERGENCIA SANITARIA POR COVID-19 -- MODELO DE REGRESIÓN DINAMICO CON DISTRIBUCIÓN HYPER-POISSON PARA SERIES DE TIEMPO DE CONTEO -- EXAMPLES OF DATA VISUALIZATION IN R, AND QUANTITATIVE METHODS FROM A SOCIAL SURVEY IN COLOMBIA -- MONITOREO DE PROCESOS NO LINEALES DE ALTA DIMENSIÓN USANDO APRENDIZAJE PROFUNDO -- CLASIFICACIÓN YANALISIS DE DATOS PROFESORALES EN UNIVERSIDADES COLOMBIANAS: UN ESTUDIO BASADO EN MINERÍA DE DATOS -- COMPARACIÓN DE ALGUNOS METODOS DE REGULARIZACION Y SELECCION, ESTUDIO DE UN CASO EN MODELOS DE REGRESION CON DISTRIBUCIÓN DE ERROR NO NORMAL -- AJUSTE DE MODELOS AUTORREGRESIVOS POISSON USANDO DESCENSO GRADIENTE -- METODO DE CLUSTER JERARQUICOS PARA DATOS FUNCIONALES MULTIVARIADOS -- ESTUDIO SOCLOECONOMICO DE LOS HABITANTES DE MEDELLIN: USANDO MODELOS DE REGRESIÓN LOGISTICOS BAYESIANOS -- ANALISIS ESPACIO-TEMPORAL PARA EL MODELAMIENTO PREDICTIVO DE LA TEMPERATURA EN COLOMBIA (2010-2022) -- MONITOREO DEL SISTEMA DE TRANSMISIÓN DE ENERGÍA ELÉCTRICA DE ENLAZA GRUPO ENERGIA BOGOTA SAS ESP, MEDIANTE CONTROL ESTADISTICO DE PROCESOS MULTIVARIADOS -- MODELOS DE REGRESIÓN ESPACIAL CON COEFICIENTES ESPACIALES VARIABLES USANDO FILTRADO DE MOKAN -- MODELO DE REGRESIÓN UNITARIO HAZARD PROPORCIONAL DOBLEMENTE CENSURADO -- MODELO DE REGRESIÓN LOGISTICO HAZARD PROPORCIONAL -- EL MODELO POWER-SKEW-NORMAL/LOGIT PARA A.JUSTAR DATOS DE PROPORCIÓN CON INFLACIÓN -- IMPLEMENTACIÓN DE LA DISTRIBUCION BURR HATKE PARA EVALUAR EL PROCESO DE ESTIMACIÓN DE PARAMETROS EN DATOS SOBRE DISPERSION -- MODELAMIENTO DE LA INCIDENCIA DE COVID-19 EN TERMINOS DE VARIABLES SOCIOECONOMICAS, DEMOGRAFICAS Y DE SALUD, USANDO REGRESIÓN SPACIAL Y MACHINE LEARNING -- CLASSIFICATION TECHNIQUES FOR IMAGINARY SPEECH BRAIN SIGNAL THROUGH SPATIAL FUNCTIONAL DATA -- PREDICCION DEL TIEMPO DE VIDA Y FRECUENCIA DE COMPRA DE CLIENTES ENLA MODALIDAD NO CONTRACTUAL -- RANDOM SPECIFIC PROJECTION DIRECTIONS FOR SKEWNESS ADJUSTED OUTLYINGNESS -- ENFOQUE INTERACTIVO Y AUTOMATIZADO DE VISUALIZACION DE DATOS PARA LA TOMA DE DECISIONES DEL ECOSISTEMA INNOVADOR EN COLOMBIA -- ANALISIS DE CORRESPONDENCIAS MULTIPLES: UNA MIRADA A LA EJECUCION DEL PLAN DE ACCION MUNDIAL SOBRE LA RESISTENCIA A LOS ANTIMICROBIANOS (RAM) -- KGBOOST CON DATOS MUCMIESPECTRALES Y ESPACIO TEMPORALES: UNA NUEVA METODOLOGIA PARA LA CLASIFICACION DE CULTIVOS DE COCA EN COLOMBIA -- COMPARACIÓN DE MODELOS DE CONTEO USANDO SIMULACIÓN PERFIL DE ACTIVIDAD FISICA CON UNA MIRADA CULTURAL DESDE UNA ESTRATEGIA STEAM PROPORCIONADA POR EL ANALISIS DE COMPONENTES PRINCIPALES (PCA), UN APRENDIZAJE AUTOMATICO DE ALGORITMO NO SUPERVISADO -- VISUALIZACIÓN DE DATOS MULTIVARIADOS EN EL SOFTWARE R PARA CARACTERIZAR VARIABLES FISICO-QUIMICAS EN FRUTAS TROPICALES CON ALGORITMOS DE AGRUPAMIIENTO -- BIOMARCADORES DE RESISTENCIA A FARMACOS EN CANCER DE OVARIO (CO): UN ESTUDIO BIBLIOMÉTRICO -- ESCALA MULTIDIMENSIONAL DE ACTITUDES ANTE EL CREDITO EDUCATIVO EN ESTUDIANTES COLOMBIANOS -- MEAN AND VARIANCE BETA REGRESSION AND BETA-BINOMIAL REGRESSION MODELS -- BINARY TWO-DIMENSIONAL IMAGE CLASSIE CATION: TENSOR REGRESSION MODEL VS TOTAL VARIATION MODEL -- TRATAMIENTO DE CLASES DESBALANCEADAS USANDO EL ALGORITMO DE DETECCIÓN DE ANOMALIAS ISOLATION FOREST -- ANALISIS DE COSTO-UTILIDAD DEL TRATAMIENTO FARMACOLÓGICO EN LAS FASES DE INDUCCION Y RECAIDA EN PACIENTES ADULTOS CON MIELOMA MÚLTIPLE EN COLOMBIA -- ANÁLISIS DE COSTO-UTILIDAD DEL TRATAMIENTO FARMACOLÓGICO EN PACIENTES CON LEUCEMIA MIEOIDE CRÓNICA EN FASE CRÓNICA -- MODELAMIENTO BAYESIANO DE LAS PREFERENCIAS POLITICAS DEL SENADO DE COLOMBIA 2006-2010: CONDUCTA ELECTORAL Y PARAPOLITICA -- METODOLOGIA ESTADISTICA ENEL DESARROLLO DEL PROYECTO: TUTOR AUTOMATICO -- MODELAMIENTO BAYESIANO DE REDES SOCIALES DE INFLUENCIA Y SU IMPACTO EN LA FORMACIÓN DE LA OPINIÓN PUBLICA -- ODELO DE REGRESIÓN BETA FUNCIONAL Y SU APLICACIÓN SOBRE LA TASA DE MORTALIDAD DEL COVID-19 -- A REVIEW OF LATENT SPACE MODELS FOR SOCIAL NETWORKS UNA REVISIÓN DE MODELOS DE ESPACIO LATENTE PARA REDES SOCIALES -- PRONOSTICO DE SERIES DE TIEMPO MULTIVARIADAS USANDO REGRESIÓN DE SOPORTE VECTORIAL -- ANALISIS BIBLIOMETRICO: TENDENCIA TEMATICA EN ESTADISTICA EN- TRE EL 200 Y 2022 -- ANALISIS DE CLASIFICACIÓN SUPERVISADA VS NO SUPERVISADA PARA DETECCIÓN TEMPRANA DE LA BACTERIA RALSTONIA EN PLANTAS DE BANANO GROS MICHEL CON BASE EN DATOS ESPECTROSCOPICOS VIS/NIR -- EXOSOMAL MICRORNA SIGNATURE FROM PLASMA-DERIVED EXTRACELTULAR VESICLES IN GASTRIC CANCER -- A SPATIAL RANDOMNESS TEST BASED ON THE BOX-COUNTING DIMENSION FOR POINT PROCESS ON LINEAK NETWORKS -- IDENTIFICACIÓN DE PATRONES TRANSCRIPCIONALES PROVOCADOS POR LA CONTAMINACION DEL AIRE Y SU ASOCIACIÓN CON ASMA GRAVER -- DETERMINANTES DE LA INNOVACIÓN EMPRESARIAL EN COLOMBIA: UNA APLICACION DE LOS MODELOS CON VARIABLE DEPENDIENTE CUALITATIVA -- APLICACIÓN DE MODELOS ESTADISTICOS PARA EL ESTUDIO DE PUPAS DEL MOSQUITO AEDES AEGYPTI EN EL DEPARTAMENTO DEL CAUCA, COLOMBIA -- POPULARIZANDO LA AGENDA 2030 Y SU RELACIÓN CON LA ESTADISTICA -- UNA ACCIÓN EXTENSIONISTA DEL PROYECTO STATUFSM -- ON THE ALMOST SURE CONVERGENCE OF THE SUCCESS PROBABILTTY ANDOTHER THEORETICAL PROPEKTIES OFA BERNOUADEBETA BAYESIAN MODEL -- CALIDAD DE DATOS, MACHINE LEARNING Y ESTADISTICA -- GEOESTADISTICA ESPACIAL NO ESTACIONARIA CON DATOS CIRCULARES -- ESTIMACION DE INDICE DE POBREZA MULTIDIMENSIONAL (IPM) EN BOGOTA D.C. Y ALGUNAS CIUDADES CERCANAS USANDO IMÁGENES SATELITALES -- DISTANCE FUNCTIONS IN THE DEFORMED EXPONENTIAL MANIFOLD INDUCED BY DIVERGENCES AND KIEMANNIAN METRICS -- ZONA DE REPORTES Y RESULTADOS DE OPINION (ZORRO) -- ANALISIS CON ENFOQUE DE GENERO DE LA DESERCION DE LOS ESTUDIANTES DE LA SEDE BOGOTA DE LA UNIVERSIDAD NACIONAL DE COLOMBIA ENTRE LOS PERIODOS 2008-3 Y 2020-1 -- AUTOEFICACIA ACADEMICA EN ESTUDIANTES UNIVERSITARIOS: UN ANALISIS SOCIOEMOCIONAL MEDIANTE TECNICAS DE MACHINE LEARNING -- LA IDENTIFICACIÓN DE LA DINAMICA DE LA TRANSFERENCIA DE CONOCIMIENTO DESDE LAS UNIVERSIDADES HACIA LAS REGIONES COLOMBIANAS -- EVOLUCIÓN DE LA CLASIFICACIÓN EN LA COMPETENCIA DE INGLES DE LAS INSTITUCIONES DE EDUCACIÓN MEDIA EN COLOMBIA: UNA APLICACIÓN DEL STATIS-ACBspa
dc.format.extent555 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.eissn2463-0861
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlrepositorio.unal.edu.cospa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/87529
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.placeBogotá, Colombiaspa
dc.relation.referencesBLANCO, Liliana; ARUNACHALAM, Viswanathan; DHARMARAJA, Selvamuthu: Introduction to probability and stochastic processes with applications. John Wiley & Sons, 2012spa
dc.relation.referencesDIEKER, Ton: Simulation of fractional Brownian motion, Masters Thesis, Department of Mathematical Sciences, University of Twente..., Tesis de Grado, 2004spa
dc.relation.referencesFRIEDRICH, Jan; GALLON, Sebastian; PUMIR, Alain; GRAUER, Rainer: Stochastic interpolation of sparsely sampled time series via multipoint fractional Brownian bridges. En: Physical Review Letters 125 (2020), Nr.17, p.170602spa
dc.relation.referencesKRANSTAUBER, Bart; KAYS, Roland; LAPOINT, ScottD.; WIKELSKI, Martin; SAFI, Kamran: A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. En: Journal of Animal Ecology 81 (2012),Nr.4,p. 738–746spa
dc.relation.referencesYUAN, Chenggui; MAO, Xuerong: Convergence of the Euler–Maruyama method for stochastic differential equations with Markovian switching. Mathematics and Computers in Simulation 64(2004),Nr.2,p.223–235spa
dc.relation.referencesChen, Y., & Georgiou, T.(2015).Stochastic bridges of linear systems. IEEE Transactions on Automatic Control,61(2),526-531.spa
dc.relation.referencesWang, S., Ramkrishna, D.,& Narasimhan, V. Exact sampling of polymer conformations using Brownian bridges. The Journal of Chemical Physics, 153 (3), 034901, 2020spa
dc.relation.referencesKranstauber, B. Modelling animal movement as Brownian bridges with covariates.spa
dc.relation.referencesCHEN, Nan; ZI, Xuemin; ZOU, Changliang. A distribution-free multivariate control chart. En: Technometrics 58 (2016), Nr. 4,p. 448–459.spa
dc.relation.referencesHAPP, Clara; GREVEN, Sonja. Multivariate functional principal component analysis for data observed on different (dimensional) domains. En: Journal of the American Statistical Association 113 (2018), Nr.522, p. 649–659.spa
dc.relation.referencesHAPP-KURZ, Clara. Object-Oriented Software for Functional Data. En: Journal of Statistical Software 93 (2020), Nr.5, p. 1–38.spa
dc.relation.referencesJANG, Jeong H. Principal component analysis of hybrid functional and vector data. En: Statistics inmedicine 40 (2021), Nr. 24, p. 5152–5173.spa
dc.relation.referencesJIANG, Qingchao; YAN, Xuefeng; ZHAO, Weixiang. Fault detection and diagnosis inspa
dc.relation.referencesMONTGOMERY, Douglas C. Introduction to statistical quality control. John Wiley & Sons, 2020.spa
dc.relation.referencesRESEARCH, Eigenvector. NIR of Corn Samples for Standardization Benchmarking. (2005).spa
dc.relation.referencesRYAN, Thomas P. Statistical methods for quality improvement. John Wiley & Sons,2011.spa
dc.relation.referencesDixon, M.F., Halperin,I., & Bilokon, P. (2020). Machine learning in finance (Vol.1170). Springer.spa
dc.relation.referencesEdelmann, D.,Fokianos, K., & Pitsillou, M.(2019). An updated literature review of distance correlation and its applications to time series. International Statistical Review, 87(2),237– 262. Wiley Online Library.spa
dc.relation.referencesChatterjee, S.(2021). A new coefficient of correlation. Journal of the American Statistical Association, 116 (536), 2009–2022.Taylor & Francis.spa
dc.relation.referencesHERNÁNDEZ ESQUIVIAS, PEDRO, Análisis de sentimientos en Twitter en castellano con redes neuronales recurrentes LSTM, B.S. thesis, 2022.spa
dc.relation.referencesHOCHREITER, SEPP y SCHMIDHUBER, JÜRGEN, Long short-term memory, Neural computation, 9 (8), 1735–1780, 1997, MIT Press.spa
dc.relation.referencesROSENBROCK, GERMÁN, TROSSERO, SEBASTIÁN, y PASCAL, ANDRÉS, Técnicas de análisis de sentimientos aplicadas a la valoración de opinión es en el lenguaje español, en XXVII Congreso Argentino de Ciencias de la Computación (CACIC) (Modalidad virtual, 4 al 8 de octubre de 2021), 2021.spa
dc.relation.referencesSHINY , Shiny for R Gallery. Available at: https://shiny.posit.co/r/gallery/ 2016spa
dc.relation.referencesCASELLA, G.,&BERGER, R.L.], Statistical Inference. Duxbury Press. 2002spa
dc.relation.referencesFREEDMAN, D.A. Statistical Models: Theory and Practice. Cambridge University Press. 2009spa
dc.relation.referencesTanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398),528–540.Taylor& Francis.spa
dc.relation.referencesSchafer, J. L.(1997). Analysis of incomplete multivariate data. CRC Press.spa
dc.relation.referencesLittle, R., & Rubin, D.(2019). Statistical analysis with missing data (Vol.793). John Wiley & Sons.spa
dc.relation.referencesLiu, C.(1994). Statistical analysis using the multivariate t distribution. Harvard University.spa
dc.relation.referencesLiu, C.(1995). Missing data imputation using the multivariate t distribution. Journal of multivariate analysis, 53(1),139–158.Elsevier.spa
dc.relation.referencesDempster, A. P., Laird, N. M.,& Rubin, D.B.(1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1),1–22. Wiley Online Library.spa
dc.relation.referencesLittle, R.J.A.(1988). Robust estimation of the mean and covariance matrix from data with missing values. Journal of the Royal Statistical Society: Series C (Applied Statistics), 37(1), 23–38. Wiley Online Library.spa
dc.relation.referencesLi, K.-H.(1988). Imputation using Markov chains. Journal of Statistical Computation and Simulation, 30(1),57–79.Taylor & Francisspa
dc.relation.referencesPEÑA, D, Analisis de datos multivariantes, Cambridge: McGraw-Hill España, 2002.spa
dc.relation.referencesALEXANDER, C, . A primer on the ortogonal GARCH model, manuscript ISMA Centre, University of Reading, UK, 2000.spa
dc.relation.referencesBOLLERSLEV, T, . A primer on the orthogonal GARCH model, Journal of econometrics, 31(3), 307-327,1986.spa
dc.relation.referencesMINUTTI, C, Métodos de optimización en la construcción de portafolios (Doctoral dissertation, Tesis de licenciatura), Universidad Autónoma Chapingo, 2010.spa
dc.relation.referencesZIVOT, E, Introduction to Computational Finance and Financial Econometrics with R, Springer,2016.spa
dc.relation.referencesBarraza M.,A.(2008). El estrés académico en alumnos de maestría y sus variables moduladoras: un diseño de diferencia de grupos. Avances en Psicología Latinoamericana. 26(2). pp. 270-289.spa
dc.relation.referencesEscobedo M, Hernández J, Estebané V ,Martínez G, (2016). Modelos de Ecuaciones Estructurales: Características, Fases, Construcción, Aplicación y Resultados. CiencTrab.18(55): 16-22.spa
dc.relation.referencesRomero, R., Babativa, G(2016). Modelo de lealtad apartir de un análisis de ecuaciones estructurales. Comunicaciones en Estadística, 9 (2), 165-197.spa
dc.relation.referencesRuiz, M.A., Pardo, A, San Martin, R. (2010), Modelos de Ecuaciones Estructurales, papeles del Psicólogo, 31(1), 34-45, Recuperado de: http://www.redalyc.org/pdf/778/77812441004.pdf.spa
dc.relation.referencesBAILEY, D.W., TROTTER, M.G. KNIGHT, C.W. AND THOMAS, M.G.(2018), Use of GPS tracking collars and accelerometers for rangel and livestock production research, Translational Animal Science 2:81–88.spa
dc.relation.referencesCASTILLA, A.L., MORA-DELGADO, J., RODRÍGUEZ-MÁRQUEZ, M.A.,&LOPEZ, J.(2022), Cattle movement as a function of some biotic and abiotic factors in atropical pasture. Revista de Ciencias Agrícolas, 39 (2), 69-88. https://doi.org/10.22267/rcia.223902.184.spa
dc.relation.referencesLI, Y.,SCHWARTZ, W.J.,&INDIC, P.(2020)., Dynamics of periodically forced finite N-oscillators, with implications for the social synchronization of animal rest–activity rhythms, Translational Animal Science 2:81–88. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(10),103106.spa
dc.relation.referencesMUÑOZ-ESPINOZA, M.,ARTIEDA-ROJAS, J.,ESPINOZA-VACA, S.,CURAY-QUISPE, S.,PÉREZ-SALINAS, M.,NÚÑEZ-TORRES, O.,&BARROS-RODRÍGUEZ, M.(2016), Granjas sostenibles: integración de sistemas agropecuarios, Tropical and Subtropical Agroecosystems, 19(2),93-99.spa
dc.relation.referencesMORA-DELGADO. J.M.,SERRANO. R.,VARÓN. R.P.,&DÍAZ. G.(2018), Use of GPS and GIS for monitoring of cattle’s grazing on a silvipasture of Tolima (Colombia), Investigaciones Andina.20(36).23-38.spa
dc.relation.referencesPOLANIA, Y.,DELGADO, J.M.,SERRANO, R.,&PIÑEROS, R.(2013), Movimiento de ganado en pastoreo en un sistema silvo pastoril del valle cálido del Magdalena tolimense (Colombia), Revista Colombiana de Ciencia Animal, 6(1).spa
dc.relation.referencesRODRIGUEZ-MARQUEZ, M.A.R.,DÍAZ, H.A.G.,&DELGADO, J.M.(2022), Behavioral fractal method associated with GPS tracking to spatial activity sequences of grazing cattle, Scientia Agricola, 80. https://doi.org/10.1590/1678-992X-2022-0052.spa
dc.relation.referencesRUSSELL, M.,BAILEY, D.,THOMAS, M.,&WITMORE, B.(2012), Grazing Distribution and Diet Quality of Angus, Brangus, and Brahman Cows in the Chihuahuan Desert, Rangeland Ecology & Management,65(4),371-381.RetrievedOctober1,2020,from http://www.jstor.org/stable/41681555.spa
dc.relation.referencesSUN, J.,BOLLT, E.M.,PORTER, M.A.,&DAWKINS, M.S.(2011), A mathematical model for the dynamics and synchronization of cows. Physica D: Non linear Phenomena, 240(19), 1497-1509.spa
dc.relation.referencesUNGAR, E.D.,HENKIN, Z.,GUTMAN, M.,DOLEV, A.,GENIZI, A.,&GANSKOPP, D.(2005), Inference of animal activity from GPS collar data on free-ranging cattle., Rangel and Ecology & Management, 58(3), 256-266. https://doi.org/10.2111/1551-5028(2005)58[256:IOAAFG]2.0.COspa
dc.relation.referencesTicona, Regina, Joe Tekli, Irvin Dongo, Renato Guzman, y Richard Chbeir. Toward RDF Normalization. 2017. http://arxiv.org/abs/1707.03602spa
dc.relation.referencesMartinez, J. ANÁLISIS DE LA FORMACIÓN DEL PRECIO DE LA VIVIENDA URBANA EN BOGOTÁ: LOCALIDADES DE KENNEDY, FONTIBÓN Y ENGATIVÁ EN EL ÚLTIMO BIENIO 2015-2016. Tesis de maestría, Instituto Geográfico Agustín Codazzi, 2016.spa
dc.relation.referencesw3c. Resource Description Framework (RDF): Concepts and Abstract Syntax. 2004. https:// www.w3.org/TR/rdf-concepts/ [Web; accedido el 06-10-2018].spa
dc.relation.referencesPappas, Alexandros, Georgia Troullinou, Giannis Roussakis, Haridimos Kondy lakis B., y Dimitris Plexousakis. The Semantic Web. Volumen 10249, 2017. Páginas 387-403. ht tp:// link. springer.com/10.1007/978-3-319-58068-5. DOI: 10.1007/978-3-319-58068-spa
dc.relation.referencesCheng, Gong, Feng Ji, Shengmei Luo, Weiyi Ge, y Yuzhong Qu. BipRank: Ranking and summarizing RDF vocabulary descriptions. Volumen 7185 LNCS, 2012. Páginas 226-241. DOI: 10.1007/978-3-642-29923-0_15.spa
dc.relation.referencesKawtrakul, Asanee, Dominique Laurent, Nicolas Spyratos, y Yuzuru Tanaka. Information search, integration, and personalization. Volumen 421 CCIS, 2014. Paginas 69-87. DOI:10.1007/978-3-319-08732-0.spa
dc.relation.referencesManolescu, Ioana. Structural Summarization of Semantic Graphs. 2018. 181spa
dc.relation.referencesZhang, Xiang, Gong Cheng, y Yuzhong Qu. Ontology summarization based on rdf sentence graph. En Proceedings of the 16th international conference on World Wide Web – WWW '07, 2007. Página 707. DOI: 10.1145/1242572.1242668.spa
dc.relation.referencesPeroni, Silvio, Enrico Motta, y Mathieu D'Aquin. Identifying key concepts in an ontology, through the integration of cognitive principles with statistical and topological measures. Volumen 5367 LNCS, 2008. Páginas 242-256. DOI: 10. 1007/978-3-540-89704-0_17.spa
dc.relation.referencesFan, Zhengjie. Concise Pattern Learning for RDF Data Sets Interlinking. Tesis de doctorado, Université, 2017.spa
dc.relation.referencesSemantic Search On Summarized RDF Triples. 2017.spa
dc.relation.referencesBasse, Adrien, Fabien Gandon, Isabelle Mirbel, y Moussa Lo. Incremental characterization of RDF Triple Stores. Volumen 24, 2012. Páginas 691201-2. https://hal. inria.fr/ hal- 00691201v2.spa
dc.relation.referencesGuzewicz, Pawel, y loana Manolescu. Quotient RDF summaries based on type hierarchies. En Proceedings - IEEE 34th International Conference on Data Engineering Workshops, ICDEW 2018, 2018. Páginas 66-7 1. DOI: 10.1109/ICDEW.2018.00018.spa
dc.relation.referencesZneika, Mussab, Claudio Lucchese, Dan Vodislav, y Dimitris Kotzinos. Summarizing Linked Data RDF Graphs Using Approximate Graph Pattern Mining. En Proc. 19th International Conference on Extending Database Technology, 2016. Páginas 684-685. DOI:10.5441/002/edbt.2016.86.spa
dc.relation.referencesBursztyn, Damian, François Goasdoué, y loana Manolescu. Optimizing Reformulation-based Query Answering in RDF. En 18th International Conference on Extending Database Technology (EDBT), 2014. Páginas 265-276. DOI: 10.5441/002/edbt.2015.24.spa
dc.relation.referencesPouriyeh, Seyedamin, Mehdi Allahyari, Qingxia Liu, Gong Cheng, Hamid Reza Arabnia, Yuzhong Qu y Krys Kochut. Graph-based Ontology Summarization: A Survey. 2018. Páginas 1-15. DOI: 10.5441/002/edbt.2015.24. http://arxiv.org/abs/1805. 06051spa
dc.relation.referencesTzitzikas, Y., D. Kotzinos y Y. Theoharis. On ranking RDF schema ele-ments (and its application in visualization). En Journal of Universal Com-puter Science, volumen 13, número 12, 2007. Páginas 1854-1880. ISSN: 09486968 (ISSN). http://www.scopus.com/inward/record.url?eid=2-s2. 0- 40849134188(&} partner ID=40(&}md5=11f36c1ed93169bec179b20aaf0a2555.spa
dc.relation.referencesCheng, Gong, Feng Ji, Shengmei Luo, Weiyi Ge y Yuzhong Qu. BipRank: Ranking and summarizing RDF vocabulary descriptions. En Lecture Notes in Computer Science (includ-ing subseries Lecture Notes in Artificial Inteligence and Lecture Notes in Bioinformatics), volumen 7185 LNCS, 2012. Páginas 226-241. DOI: 10.1007/978-3-642-29923-0_15.spa
dc.relation.referencesCheng, Gong, Weiyi Ge y Yuzhong Qu. Generating summaries for ontology search. En Pro-ceedings of the 20th international conference companion on World wide web - WWW '11, 2011. Página 27. DOI: 10.1145/1963192.1963207. http://portal. acm.org/citation. ctm?doid=1963192. 1963207.spa
dc.relation.referencesDrumond, Lucas, Steffen Rendle y Lars Schmidt-Thieme. Predicting RDF triples in incomplete knowledge bases with tensor factorization. En Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12, 2012. Página 326. DOI: 10.1145/2245276.2245341. http://dl.acm. org/citation. cfm?doid=22452762246341spa
dc.relation.referencesSebiric, Sejla, François Goasdoué y loana Manolescu. Query-Oriented summarization of RDF graphs. En Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volumen 9147, 2015. Paginas 87-91. DOI: 10.1007/978-3-319-20424-6_9.spa
dc.relation.referencesZneika, Mussab, Dan Vodislav, Dimitris Kotzinos y Quality Metrics For RDF Graph Summarization. Quality Metrics For RDF Graph Summarization. 2018.spa
dc.relation.referencesSu, Xiang, Hao Zhang, Jukka Riekki, Ari Keränen, Jukka K. Nurminen y Libin Du. Connect-ing loT sensors to knowledge-based systems by transforming SenML to RDF. En Procedia Computer Science, volumen 32, 2014. Páginas 215-222. DOI: 10.1016/j procs.2014.05.417.spa
dc.relation.referencesHassad, Sara El. Learning Commonalities in RDF. 2017. DOI: 10.1007/978-3-319-58068-5.spa
dc.relation.referencesAyvaz., Serkan y Aydar, Mehmet. Using RDF Summary Graph For Keyword-based Seman-tic Searches. 2017. DOI: 10.1109/COMPSAC.2015.107. http://arxiv. org/abs/1707. 03602spa
dc.relation.referencesDiscipline, S T a P S, Cnu, Section, Vincent, Chaubet, Richard, Montoya y Nicolas, Forestier. Université de pau et des pays de l'adour. 2011.spa
dc.relation.referencesZliobaite, Indre, Leaming under Concept Brif: an Overview. 2010. DOT: 10.1002/sam. http://arxiv.org/abs/1010.4784spa
dc.relation.referencesTicona-herrera, Regina, Tekli, Joe, Dongo, Irvin, Guzman, Renato y Chbeir, Richard. Toward RDF Normalization.spa
dc.relation.referencesManolescu, Ioana. Structural Summarization of Semantic Graphs. 2018.spa
dc.relation.referencesZhu, Ganggao y Iglesias, Carlos A. Sematch: Semantic similarity framework for Knowledge Graphs. 2017. DOI: 10.1016/j.knosys.2017.05.02l. UNAL https://www.simposioestadistica.unal.edu.co/spa
dc.relation.referencesHees, Jörn, Bauer, Rouven, Folz, Joachim, Borth, Damian y Dengel, Andreas. An evolutionary algorithm to learn SPARQL queries for source-target-pairs finding pat-terns for human associations in DBpedia. 2016. DOI: 10.1007/978-3-319-49004-5_22. http://arxiv.org/abs/1607.07249spa
dc.relation.referencesDe Vries, Gerben Klaas Dirk y De Rooij, Steven. Substructure counting graph kernels for machine learning from RDF data. 2015. DOI: 10.1016/j.websem.2015.08.002.spa
dc.relation.referencesXylogiannopoulos, Konstantinos F, Karampelas, Panagiotis y Alhajj, Reda. Dynamic Pattern Detection for Big Data Stream Analytics.spa
dc.relation.referencesPotoniec, Jedrzej. An on-line learning to query system. 2016.spa
dc.relation.referencesHayes, Jonathan y Gutierrez, Claudio. Bipartite Graphs as Intermediate Model for RDF. 2004. DOI: 10.1007/978-3-540-30475-3_5.spa
dc.relation.referencesAyvaz, Serkan, Aydar, Mehmet y Melton, Austin. Building Summary Graphs of RDF Data in Semantic Web. 2015. DOI: 10.1109/COMPSAC.2015.107.spa
dc.relation.referencesAmaout, Hiba y Elbassuoni, Shady. Effective searching of RDF knowledge graphs. 2018. DOI: 10.1016/j.websem.2017.12.001.spa
dc.relation.referencesCebiric, Sejla, Goasdoué, François y Manolescu, Ioana. A framework for eficient represen-tative summarization of RDF graphs. 2017.spa
dc.relation.referencesAydar, Mehmet, Ayvaz, Serkan y Melton, Austin. Automatic weight generation and class predicate stability in RDF summary graphs. 2015./spa
dc.relation.referencesHettmansperger, T. (1984). Statistical Inference Based on Ranks. John Wiley & Sons, New York.spa
dc.relation.referencesLehmann, E. L., & D'A brera, H. J. (1975). Nonparametrics: Statistical Methods Based on Ranks. Holden-Day.spa
dc.relation.referencesShuster, J. J. (2019). CRC Handbook of Sample Size Guidelines for Clinical Trials. CRC Press.spa
dc.relation.referencesMcCrum-Gardner, E. (2008). Which Is the Correct Statistical Test to Use?. British Journal of Oral and Maxillofacial Surgery, 46(1), 38-41. Elsevier.spa
dc.relation.referencesMcWilliams, T. P. (1990). A Distribution- Free Test for Symmetry Based on a Runs Statistic.spa
dc.relation.referencesJournal of the American Statistical Association, 85(412), 1130-1133. Taylor & Francis Group.spa
dc.relation.referencesHall, P., & Heyde, C. C. (2014). Martingale Limit Theory and Its Application. Academic Press.spa
dc.relation.referencesBaklizi, A. (2003). A Conditional Distribution Runs Test for Symmetry. Journal of Nonpara-metric Statistics, 15(6), 713-718.spa
dc.relation.referencesRocha, P. (1994). Estadística de Prueba para Problemas de Localización Basada en Rachas. Tesis de Maestría, Universidad Nacional de Colombia, Facultad de Ciencias, Departamento de Estadística, Bogotá.spa
dc.relation.referencesRandles, R. H., & Wolfe, D. A. (1979). Introduction to the Theory of Nonparametric Statistics. John Wiley.spa
dc.relation.referencesDIRECCIÓN DE POLÍTICAS Y ESTRATEGIA, Fiscalía General de la Nación, 2023.spa
dc.relation.referencesARRIETA-BURGOS, E., DUQUE-PEDROZA, A. Y DíEz-RUGELES, M., Delitos sexuales en contra de menores de edad en Colombia: caracterización criminológica y políticocriminal, Revista Criminalidad, 62(2): 247-274, 2020.spa
dc.relation.referencesNORZA-CÉSPEDES, ERVYN, Y ESPINO-DUQUE, GLORIA PATRICIA., Criminalidad y análisis espacial de los delitos en Colombia, 2010, Revista Criminalidad, 53(1), 17-43, 2011.spa
dc.relation.referencesMEJIA, D; ORTEGA, D; ORTIz, K., Un análisis de la criminalidad urbana en Colombia, Banco de desarrollo de América Latina, 2015.spa
dc.relation.referencesCHAZAL F., FASY, B. (2014) CONFIDENCE SETS FOR PERSISTENCE DIAGRAMS. Annals of Statistics, Vol. 42, No. 6, 2301-2339.spa
dc.relation.referencesJIsu KIM, BRITTANY TERESE FASY AND FABRIZIO LECCI (2015) Tutorial on the R package TDA., Journal of Symbolic Computationspa
dc.relation.referencesPETER BUBENIK (2023) Department of Mathematics. University of Floridaspa
dc.relation.referencesJolliffe, Ian T. (2002). Principal component analysis for special types of data. Springer.spa
dc.relation.referencesHastie, Trevor, Tibshirani, Robert, Friedman, Jerome H. (2009). The elements of statistical learning: data mining, inference, and prediction, Vol. 2. Springer.spa
dc.relation.referencesVan der Maaten, Laurens, Hinton, Geoffrey. (2008). "Visualizing data using t-SNE." Journal of machine learning research, 9(11).spa
dc.relation.referencesMclnnes, Leland, Healy, John, Melville, James. (2018). "Umap: Uniform manifold approx-imation and projection for dimension reduction." arXiv preprint arXiv: 1802.03426.spa
dc.relation.referencesPramanik, Md, Rahman, Md Mahbubur, Anam, ASM, Ali, Amin Ahsan, Amin, M Ashraful, Rahman, AKM, and others. (2021). "Modeling Traffic Congestion in Developing Countries using Google Maps Data." In Future of Information and Communication Conference, pp. 513-531. Springer.spa
dc.relation.referencesWu, Tianlong, Chen, Feng, Wan, Yun. (2018). "Graph attention LSTM network: A new model for traffic flow forecasting." In 2018 Sth International Conference on Information Science and Control Engineering (ICISCE), pp. 241-245. TEEE.spa
dc.relation.referencesUnited Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. Disponible en: https://sustainabledevelopment.un.org/post2015/ transformingourworldspa
dc.relation.referencesWorld Health Organization (WHO). (2021). Ambient air pollution: Health impacts. Disponible en: https://www.who.int/health-topics/ambient-air-pollution# tab=tab_spa
dc.relation.referencesMcVean, Gil. (2009). "A genealogical interpretation of principal components analysis." PLoS genetics, 5(10), e 1000686.spa
dc.relation.referencesVelliangiri, S, Alagumuthukrishnan, SJPCS, and others. (2019). "A review of dimensionality reduction techniques for efficient computation." Procedia Computer Science, 165, 104-111. Elsevier.spa
dc.relation.referencesIgnacio Pérez, José Ricardo Cure, Néstor Monroy. Modelo de predicción y manejo de cultivos de rosas. Keywords: Grado - día, Cosecha, Invernadero.spa
dc.relation.referencesMark Everingham, Luc Van Gool, C. K. I. Williams, J. Winn, Andrew Zisserman. The PASCAL Visual Object Classes (VOC) challenge. (2010).spa
dc.relation.referencesAndrew Engel. Feature Engineering for Time Series Data. Keywords: Feature Engineering for Time Series Dataspa
dc.relation.referencesRubén Costas, Patricio Mac Donagh, Elizabeth Weber, Santiago Figueredo, Claudio Gómez, Pedro Irschick. Modelos predictivos de la producción de Pinus taeda empleando variables vinculadas con las podas. Bosque (Valdivia), 27(2), 2006. DOI: 10.4067/s0717-92002006000200004.spa
dc.relation.referencesManual de observaciones fenológicas, Servicio Nacional de Meteorología e Hidrología (Senanhi), Perú, 2011.spa
dc.relation.referencesFrancesca Lazzeri. Introduction to feature engineering for time series forecasting. (2021).spa
dc.relation.referencesRob J Hyndman, George Athanasopoulos. Forecasting: Principles and Practice; Classical Decomposition, 2021.spa
dc.relation.referencesAlex Mitrani. Time Series Decomposition and Statsmodels Parameters, 2020.spa
dc.relation.referencesCharles Potters. Variance Inflation Factor (VIF), 2021.spa
dc.relation.referencesBenjamin Kedem, Konstantinos Fokianos. Regression Models for Time Series Analysis. Wiley- Interscience, 2002.spa
dc.relation.referencesA lan McLeod. Time Series Analysis: Methods and Applications Volume 30. Time Series Analysis with R. Elsevier, 2012. DOI: 10.1016/B978-0-444-53858-1.00023-5.spa
dc.relation.referencesWalter Zucchini, lain MacDonald. Hidden Markov Models for Time Series: An Introduction Using R. Chapman and Hall/CRC, 2009.spa
dc.relation.referencesSam Ewen. The Poisson Distribution.spa
dc.relation.referencesSachin Date. An Illustrated Guide to the Poisson Regression Model.spa
dc.relation.referencesSachin Date. Negative Binomial Regression: A Step by Step Guide.spa
dc.relation.referencesNCSS Statistical Software. Negative Binomial Regression.spa
dc.relation.referencesA. Colin Cameron, Pravin K. Trivedi. Regression analysis of count data. Cambridge University Press, 2013.spa
dc.relation.referencesCFI Corporate Financial Institute. Overfitting - A modeling error that occurs when a function corresponds too closely to a particular set of data.spa
dc.relation.referencesShaun Turney. Coefficient of Determination (R squared) | Calculation & Interpretation.spa
dc.relation.referencesRohan Joseph. Grid Search for model tuning.spa
dc.relation.referencesGareth James, Daniela Witten, Trevor Hastie. An Introduction to Statistical Learning: With Applications in R. Springer Nature, 2017.spa
dc.relation.referencesJames Franklin. The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 2005. DOI: 10.1007/bf02985802.spa
dc.relation.referencesEmelina López, Marcos Ruiz. Análisis de datos con el Modelo Lineal Generalizado. Una aplicación con R. Revista española de pedagogía, 2011.spa
dc.relation.referencesJerome Friedman, Trevor Hastie, Robert Tibshirani. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 2010. DOI: 10.18637/jss.v033.i01.spa
dc.relation.referencesSkipper Seabold, Josef Perktold. statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference, 2010.spa
dc.relation.referencesR: A Language and Environment for Statistical Computing, R Core Team, 2022.spa
dc.relation.referencesRobert E Fay III, Roger A Herriot. Estimates of income for small places: an application of James-Stein procedures to census data. Journal of the American Statistical Association, 74(366a), 1979.spa
dc.relation.referencesRichard Valliant. Generalized variance functions in stratified two-stagy sampling. Journal of the American Statistical Association, 82(398), 1987.spa
dc.relation.referencesH. S. Gutiérrez. Estrategias de Muestreo: Diseño de encuestas y estimación de parámetros. Ediciones de la U, 2016.spa
dc.relation.referencesIGAC, Suelos y Tierras de Colombia. Instituto Geográfico A gustín Codazzi, IGAC., 2016.spa
dc.relation.referencesTHOMPSON L. M. AND TROEH F. R, Soils and Fertility. 1980.spa
dc.relation.referencesHARVEY D., Teorías, leyes y modelos en geografta. Alianza Editorial, 1969.spa
dc.relation.referencesPABÓN J. & MONTEALEGRE J., Los Fenómenos de El Niño Y de la Niña. Bogotá, Editorial Gente Nueva, 2017. [5] CRESSIE N., Statistics for Spatial Data. New York: Jhon Wiley & Sons, 1993.spa
dc.relation.referencesTOBLER W., A Computer Movie Simulating Urban Growth in the Detroit Region, Economic Geography, Supplement: Proceedings. International Geographical Union. Commission on Quantitative Methods, vol. 46, pp. 234-240, 1970.spa
dc.relation.referencesUNWIN D., Introductory Spatial Analysis. New York: Methuen & Co., 1981.spa
dc.relation.referencesRHIND D. W., A skeletal overview of spatial interpolation techniques, Computer Applica-tions, vol. 2, pp. 293-309, 1975.spa
dc.relation.referencesISAAKS E. H. & SRIVASTAVA M., Applied Geostatistics. New York: Oxford University Press, Inc., 1989spa
dc.relation.referencesJOURNEL A. G. & HUIBREGTS CH. J, Mining Geoestatistics. Francia: Harcourt Brace Jnovich, Publishers, 1978.spa
dc.relation.referencesGIRALDO R., Introducción a la Geoestadistica, Teoría y Aplicación. Bogotá, D.C. : Universidad Nacional de Colombia, 2011.spa
dc.relation.referencesGIRALDO R, Geoestadística con Datos Funcionales. Conferencia, 32° Simposio Internacional de Estadística, Bioestadística y Datos Funcionales, Aug. 03, 2023.spa
dc.relation.referencesSIABATO W., Técnicas de Análisis Modelado Espacial (TAME). Notas de apoyo en clase. Bogotá D.C., Colombia: Universidad Nacional de Colombia, 2023.spa
dc.relation.referencesCRESSIE N. & C. WIKLE, Statistics for Spatio-Temporal Data. New Jersey, USA y Canada : Wiley & Sons, Inc, 2011.spa
dc.relation.referencesHARVEY D., Social justice and the city. Johns Hopkins University Press, 1973.spa
dc.relation.referencesWATERS, N., Unit 40 - Spatial interpolation 1, 1990.spa
dc.relation.referencesSTEEN F. H. & BALLOU D. H., Geometría Analítica. México D.F: Publicaciones Cultural, S. A., 1966.spa
dc.relation.referencesDE IACO, S. & CAPELLO, C., Spatio-temporal modeling and prediction, ISI Online Course: Spatio-temporal modeling and prediction, 2023.spa
dc.relation.referencesJHONSTON K., VER HOEF J., KRIVORUCHYO, K. & LUCAS, N., Using Arcgis Geostatis-tical Analyst, GIS by ESRI, United States of America, 2001.spa
dc.relation.referencesDíAz-FRANCÉS., Introducción a Conceptos Básicos de Geoestadística, Memorias Seminario Estadística y Medio Ambiente, Guanajuato, México, Centro de Investigación en Matemáticas, CIMAT, 1993.spa
dc.relation.referencesNowicKI, K. AND SNIDERS, T. A. B., Estimation and prediction for stochastic block-structures. Journal of the American statistical association, 96(455): 1077-1087, 2001.spa
dc.relation.references] KOLACZYK, E. D. AND CÁRDI, G., Statistical analysis of network data with R, Springer, Volume 65, 2020.spa
dc.relation.referencesHASELMAYER, M. AND JENNY, M., Sentiment analysis of political communication: Combining a dictionary approach with crowdcoding, Quality & Quantity, 51:2623-2646. 2017.spa
dc.relation.referencesampo Elías Pardo. Estadística Descriptiva Multivariada. Universidad Nacional de Colom-bia. Sede Bogotá, 2020.spa
dc.relation.referencesEncuesta Nacional de Calidad de Vida. DANE, 2023. Agencia Nacional de Datos Abiertos.spa
dc.relation.referencesDANE. Encuesta Nacional de Calidad de Vida. 2023. Disponible en: https:// microdatos.dane.gov.co/index. php/catalog/POBCONVIDspa
dc.relation.referencesGlenn C. Blomquist. Measuring Quality of Life. A Companion to Urban Economics, 2006. DOl: https://doi.org/10.1002/9780470996225. ch28spa
dc.relation.referencesBilver Astorquiza-Bustos, Kevin Bravo-Bolaño, Enmanuel Aguirre-Bernal. Indice de precariedad laboral en Colombia: una construcción teórica y analítica a partir de mi-crodatos. Revista Mexicana de Economía y Finanzas Nueva Época REMEF, vol. 18, no. 1, 2022. Palabras clave: Precariedad laboral, microdatos, metodología de conjuntos di-fusos, Colombia. Resumen y DOI: https://www.remef.org.mx/index.php/remef/ article/view/822spa
dc.relation.referencesIDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales). (s.f.). Contami-nación y calidad ambiental.spa
dc.relation.referencesHolmes, J., Hassini, S. Discrete-Time Markov Chain Modelling of the Ontario Air Quality Health Index. Water Air Soil Pollut 232, 158 (2021).spa
dc.relation.referencesBlanco-Castañeda, L., & Arunachalam, V. (20J3). Applied Stochastic Modeling. Springer.spa
dc.relation.referencesHolmes, J., Hassini, S. Discrete-Time Markov Chain Modelling of the Ontario Air Quality Health Index. Water Air Soil Pollut 232, 158 (2021). https://www.paho.org/es/temas/ calidad-aire/calidad-aire-ambientespa
dc.relation.referencesIBOCA (Índice Bogotano de Calidad del Aire). (s.f.). ¿Qué es el IBOCA? Recuperado de http://iboca.ambientebogota.gov. co/publicaciones/175/que-es-el-iboca/spa
dc.relation.referencesDÍAz, F., y HERNÁNDEz, G., Estrategias docentes para un aprendizaje significativo: una interpretación constructivista (2" ed.), Mc Graw Hill, 2002.spa
dc.relation.referencesCABERO, J., Las TIC y las Universidades: retos, posibilidades y preocupaciones, Revista de la Educación Superior, 34 (3), 77-100.spa
dc.relation.referencesHAIR, J., HULT, T., RINGLE, C. Y SARSTED, M., A primer on partial least square structural equation modeling (PLS-SEM) (2" ed.) , Sage Publications,2017.spa
dc.relation.referencesSÁNCHEZ-OTERO, M., GARCÍA-GUILIANY, J., STEFFENS-SANABRIA, E., Y PALMA, H. H., Estrategias pedagógicas en procesos de enseñanza y aprendizaje en la educación superior incluyendo tecnologías de la información y las comunicaciones, Información Tecnológica, 30(3), 277-286.spa
dc.relation.referencesVARGAS, C. G. J., La investigación prixeológica: un enfoque alternativo, Praxis Pedagóg-ica, 20(26), , 117-148.spa
dc.relation.referencesMadden, L.V., Hughes, G., & Bosch, F.V. Spatial aspects of epidemics II: A theory of spatio-temporal disease dynamics. En: The Study of Plant Disease Epidemics. APS Press - The American Phytopathological Society, St. Paul, Minnesota, U.S.A., 2007. 421 pp. ISBN 978-0-89054-354-2.spa
dc.relation.referencesGrubesic, Tony H, Wei, Ran y Murray, Alan T, Spatial clustering overview and comparison: Accuracy, sensitivity, and computational expense, Annals of the Association of American Geographers, vol. 104, no. 6, pp. 1134-1156, 2014, Taylor & Francis.spa
dc.relation.referencesGrubesic, Tony H, Wei, Ran y Murray, Alan T, Spatial clustering overview and comparison: Accuracy, sensitivity, and computational expense, Annals of the Association of American Geographers, vol. 104, no. 6, pp. 1134-1156, 2014, Taylor & Francis.spa
dc.relation.referencesSiabato, Willington y Guzmán-Manrique, Jhon, Spatial autocorrelation and the development of quantitative geography, Cuadernos de Geografta: Revista Colombiana de Geografia, vol. 28, no. 1, pp. 1-22, 2019.spa
dc.relation.referencesSiabato, Willington y Guzmán-Manrique, Jhon, Spatial autocorrelation and the development of quantitative geography, Cuadernos de Geografta: Revista Colombiana de Geografia, vol. 28, no. 1, pp. 1-22, 2019.spa
dc.relation.referencesEsri, Spatial Statistics Resources, Disponible en: https://spatialstats-analysis-1/ hub.arcgis.com/, 2021.spa
dc.relation.referencesDavid R. Cagna, Terence E. Donovan, James R. McKee, Frederick Eichmiller, James E. Metz, Jean-Pierre Albouy, Ricardo Marzola, Kevin G. Murphy y Matthias Troeltzsch, "Annual review of selected scientific literature: A report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry," The Journal of Prosthetic Dentistry, vol. 126, no. 3, pp. 276-359, 2021. DOI: https://doi.org/10.1016/j.prosdent.2021 06.014.spa
dc.relation.referencesE. L. Kaplan y Paul Meier, "Nonparametric Estimation from Incomplete Observations," Journal of the American Statistical Association, vol. 53, no. 282, pp. 457-481, 1958. URL: http://www.jstor.org/stable/2281868 (visitado el 19-07-2023).spa
dc.relation.referencesDavid G. Kleinbaum y Mitchel Klein, Survival Analysis: A Self-Learning Text, Springer New York, 2012.spa
dc.relation.referencesDavid Machin, Yin Bun Cheung y Mahesh Parmar, Survival Analysis: A Practical Approach, 2nd ed., Wiley, 2023.spa
dc.relation.referencesTerry M. Thereau y The R Foundation, Survival Analysis in R, marzo de 2023. URL: https://cran.rproject.org/web/packages/survival/index.html (visitado el 24- 03-2023).spa
dc.relation.referencesJ. P. Van Nieuwenhuysen, W. D'Hoore, J. Carvalho y V. Qvist, "Long-term evaluation of extensive restorations in permanent teeth," J Dent, vol. 31, no. 6, pp. 395-405, 2003. DOI: 10. 1016/s0300-5712 s0300-5712(03) 00084-8spa
dc.relation.referencesGail Potter, Jimmy Wong, Irvin Alcaraz, Peter Chi, y otros, Web application teaching tools for statistics using R and shiny. Technology Innovations in Statistics Education, vol. 9, no. 1, 2016.spa
dc.relation.referencesAndrew Gelman, John B Carlin, Hal S Stern y Donald B Rubin, Bayesian data analysis. Chapman and Hall/CRC, 1995.spa
dc.relation.referencesYen-Ting Lin y Min Jou, Integrating popular web applications in classroom learning environments and its effects on teaching, student learning motivation and performance. Turkish Online Journal of Educational Technology-TOJET, vol. 12, no. 2, pp. 157-165, 2013.spa
dc.relation.referencesGil Perry, Silla Blondheim y Eliran Kuta, D-ID, IA generativa. Recuperado julio de 2023, de https://www.d-id.com/ 1 2023.spa
dc.relation.referencesNATIONAL HEART, LUNG AND BLOOD INSTITUTE, Pneumonia Recovery, 2022. Recuper-ado de https://www.nhlbi.nih.gov/health/pneumonia/recover.spa
dc.relation.referencesR CoRE TEAM, R: A Language and Environment for Statistical Computing, 2020. URL = https://www.R-project.org/spa
dc.relation.referencesRos-GUTIÉRREZ A, To https://www.R-project.org/AM V. • (2023) An updated estimation approach for SEIR models wun swenuse penurbations: Application to COVID-19 data in Bogotá. PLOS ONE 18(8): e0285624. htups://doi. org/ 10. 1371/journal pone 0285624spa
dc.relation.referencesSoumyya Kanti Datta, Mohammad Abuzar Shaikh, Sargur N Srihari y Mingchen Gao, "Soft attention improves skin cancer classification performance," en Interpretabiliry of Machine Intelligence in Medical Image Computing, and Topological Data Analysis and Its Applications for Medical Data: 4th International Workshop, iMIMIC 2021, and Ist International Workshop, TDA4MedicalData 2021, Held in Conjunction with MICCAT 2021, Strasbourg, France, September 27, 2021, Proceedings 4, pp. 13-23, 2021, Springer.spa
dc.relation.referencesGiona Kleinberg, Michael J Diaz, Sai Batchu y Brandon Lucke-Wold, "Racial under-representation in dermatological datasets leads to biased machine learning models and inequitable healthcare," en Journal of biomed research, vol. 3, no. 1, p. 42, 2022, NIH Public Access.spa
dc.relation.referencesMaryBeth B Culp y Natasha Buchanan Lunsford, "Melanoma among non-Hispanic black Americans," en Preventing Chronic Disease, vol. 16, 2019, Centers for Disease Control and Prevention.spa
dc.relation.referencesAndres MORALES-FORERO, Lili RUEDA, Sebastian GIL-QUINONEZ, M BARRERA, Samuel BASSETTO y Eric COATANEA, "AN INSIGHT INTO RACIAL BIAS IN DER-MOSCOPY REPOSITORIES: A HAM10000 DATASET ANALYSIS," presentado en Journal of the European Academy of Dermatology and Venereology, agosto de 2023.spa
dc.relation.referencesInternational Skin Imaging Collaboration y otros, "Siim-isic 2020 challenge dataset," en International Skin Imaging Collaboration, 2020.spa
dc.relation.referencesMarc Combalia, Noel CF Codella, Veronica Rotemberg, Brian Helba, Veronica Vilaplana, Ofer Reiter, Cristina Carrera, Alicia Barreiro, Allan C Halpern, Susana Puig y otros, "Ben20000: Dermoscopic lesions in the wild," en arXiv preprint ar Xiv:1908.02288, 2019.spa
dc.relation.referencesPhilipp schandl, Cliff Rosendahl y Harald Kittler, "The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,' " en Scientific data, vol. 5, no. 1, Pp. 1-9, 2018, Natere Publishing Group.spa
dc.relation.referencesJose L Cortez, Juan Vasquez y Maria L. Wei, "The impact of demographics, socioeconomics, and health care access on melanoma outcomes," en Journal of the American Academy of Dermatology, vol. 84, no. 6, pp. 1677-1683, 2021, Elsevier.spa
dc.relation.referencesZehra Rizvi, Viktor Kunder, Hanna Stewart, Paola Torres, Sana Moon, Nimisha Lingappa, Mallory Kazaleh, Varshini Mallireddigari, Julian Perez, Nigel John y otros, "The bias of physicians and lack of education in patients of color with melanoma as causes of increased mortality: a scoping review," en Cureus, vol, 14, no. 11, 2022, Cureus.spa
dc.relation.referencesJC Lester, JL. Jia, L Zhang, GA Okoye y E Linos, *Absence of images of skin of colour in publications of COVID-19 skin manifestations," en British Journal of Dermatology, vol. 183, no. 3, pp. 593-595, 2020, Blackwell Publishing Ltd Oxford, UK.spa
dc.relation.referencesPatricia Patricia Louie y Rima Wilkes, "Representations of race and skin tone in medical textbook imagery," en Social Science & Medicine, vol. 202, PP. 38-42, 2018, Elsevier.spa
dc.relation.referencesMina Kim, Susan L Boone, Dennis P West, Alfred W Rademaker y Roopal V Kundu, "Perception of skin cancer risk by those with ethnic skin," en Archives of dermatology, vol. 145, no. 2, pp. 207-208, 2009, American Medical Association.spa
dc.relation.references"Why the Color of Your Skin Can Affect the Quality of Your Diagnosis," https://www.improvediagnosis.org/dxiq-column/ why-the-color-of-your-skin-can- affect-the-quality-of-your-diagnosis/ consultado el 01-06-2023.spa
dc.relation.referencesSean M Dawes, Sheena Tsai, Haley Gittleman, Jill S Barnholtz-Sloan y Jeremy S Bor-deaux, "Racial disparities in melanoma survival," en Journal of the American Academy of Dermatology, vol. 75, no. 5, pp. 983-991, 2016, Elsevier.spa
dc.relation.referencesJoshua Brady, Reem Kashlan, Julie Ruterbusch, Mehdi Farshchian y Meena Moossavi, "Racial disparities in patients with melanoma: a multivariate survival analysis," en Clinical, Cosmetic and Investigational Dermatology, PP. 547-550, 2021, Taylor & Francis.spa
dc.relation.referencesHugh M Gloster Jr y Kenneth Neal, "Skin cancer in skin of color," en Journal of the American Academy of Dermatology, vol. 55, no. 5, pp. 741-760, 2006, Elsevier.spa
dc.relation.referencesU.S. Census Bureau Newsroom, https://www.census-gov/newsroom/releases/ archives/population/ cb12-243. html, consultado el 01-06-2023.spa
dc.relation.referencesMona A Gohara, "Skin cancer in skins of color," en Journal of drugs in dermatology: JDD, vol. 7, no. 5, pp. 441-445, 2008.spa
dc.relation.referencesXiao-Cheng Wu, Melody J Eide, Jessica King, Mona Saraiya, Youjie Huang, Charles Wiggins, Jill S Barnholtz-Sloan, Nicolle Martin, Vilma Cokkinides, Jacqueline Miller y otros, "Racial and ethnic variations in incidence and survival of cutaneous melanoma in the United States, 1999-2006," en Journal of the American Academy of Dermatology, vol. 65, no. 5, pp. S26-el, 2011, Elsevier.spa
dc.relation.referencesStephen J Merrill, Madhan Subramanian y Dianne E Godar, "Worldwide cutaneous malignant melanoma incidences analyzed by sex, age, and skin type over time (1955-2007): Is HPV infection of androgenic hair follicular melanocytes a risk factor for developing melanoma exclusively in people of European-ancestry?," en Dermato-endocrinology, vol. 8, no. 1. p. e1215391, 2016, Taylor & Francis.spa
dc.relation.referencesAlpana K Gupta, Mausumi Bharadwaj y Ravi Mehrotra, Skin cancer concerns in people of color: risk factors and prevention," en A sian Pacific journal of cancer prevention: APJCP, vol. 17, no. 12, p. 5257, 2016, Shahid Beheshti University of Medical Sciences.spa
dc.relation.referencesAndres Morales-Forero, Samuel Bassetto y Eric Coatanea, "Toward safe AI," en Al & SOCIETY, vol. 38, no. 2, pp. 685-696, 2023, Springer.spa
dc.relation.referencesCynthia Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead," en Nature machine intelligence, vol. I, no. 5, pp. 206-215, 2019, Nature Publishing Group UK London.spa
dc.relation.referencesELCA 2016 RONDA 3. Bases de datos Rural, formulario personas. Tomado de https: //encuestalongitudinal. uniandes.edu. co/es/spa
dc.relation.referencesPARDO, C. E., (2020). Estadística descriptiva multivariada. Universidad Nacional.spa
dc.relation.referencesRAMONI, J. Y ORLANDONI, G., (2016). Análisis de la estructura del mercado laboral en Colombia: un estudio por género mediante correspondencias múltiples. Universidad de Santander. Recuperado de https://repositorio.uam.es/bitstream/handle/10486/ 690541/CB_113_2. pdfspa
dc.relation.referencesMAYORGA, M.,Caracterización ecofisiológica de curuba (Passiflora tripartita var. Mollis-sima) en dos condiciones ambientales, Tesis de maestría en Ciencias A grarias, Universidad Nacional de Colombia, Bogota, 2016.spa
dc.relation.referencesR CORE TEAM, R: A Language and Environment for Statistical Computing, Vienna, Austria, R Foundation for Statistical Computing, 2023. url - https://www.R-project.org/spa
dc.relation.referencesMARDIA, K. V. AND KENT, J. T. AND BIBBY, J. M., Multivariate analysis, London, Academic Press, 2002.spa
dc.relation.referencesGOWER, J. C. AND KRZANOWSKI, W.J., Analysis of distance for structured multivariate data and extensions to multivariate analysis of variance, Applied statistics, 48(4), 505-519, 1999.spa
dc.relation.referencesC. Zelada, "Evaluación de modelos de clasificación." Recuperado de https://rpubs.com/ chzelada/275494, 2017. Online.spa
dc.relation.referencesHaolun S., Yuping Y., Liangliang W., Da M., Mirza F. B., Jian P. y Jiguo C., "Two-Dimensional Functional Principal Component Analysis for Image Feature Extraction.* Recuperado de https://doi.org/10.1080/10618600.2022. 2035738, 2022. Online.spa
dc.relation.referencesM.E. Arrieta, "Material de clase Analisis de regresión. Semana 17." 2023. Online.spa
dc.relation.referencesNavoneel Chakrabarty, "Brain MRI Images for Brain Tumor Detec- tion." Recuperado de https://www.kaggle.com/datasets/navonee1/ brain-mri-images-for-brain-tumor-detection, 2019. Online.spa
dc.relation.referencesJ.0. Ramsay y B.W. Silverman, "Functional Data Analysis." Springer, vol. 322, no. 2, 2005. (7] A. Géron, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow." O'REILLY, no. 2, 2019.spa
dc.relation.referencesDE AMO, E., DÍAZ CARRILLO, M., DURANTE, F., FERNÁNDEZ SÁNCHEZ, J., Extensions of subcopulas, Journal of Mathematical Analysis and Applications 452 (1): 1 - 15, 2017spa
dc.relation.referencesDURANTE, F. AND SEMPI, C., Principles of Copula Theory, CRC press, 2016spa
dc.relation.referencesERDELY, A., A subcopula based dependence measure, Kybernetika 53 (2), 231 - 243, 2017spa
dc.relation.referencesJOE, H., Dependence Modeling with Copulas, CRC Press, 2014spa
dc.relation.referencesNELSEN, R. B., An Introduction to Copulas, Springer, New York, 2006spa
dc.relation.referencesOrganización Mundial de la Salud y Banco Mundial: Informe mundial sobre la discapacidad. 11. https://www.oas.org(2011)spa
dc.relation.referencesPardo, Campo Elías: Estadística descriptiva multivariada, Bogotá, Colombia: Universidad Nacional de Colombia. 2020spa
dc.relation.referencesPineda Duque, J. A., Luna Ruiz, A: Intersecciones de género y discapacidad. La inclusión laboral de mujeres con discapacidad, Sociedad y Economía, (35), 158-181. https://doi.org/ 10.25100/sye.v0i35.5652(2018)spa
dc.relation.referencesUniversidad de los Andes: Encuesta Longitudinal Colombiana: Formularios Urbano adultos y Urbano Hogar, 2016.spa
dc.relation.referencesD. Kosiorowski, J. P. Rydlewski, M. Snarska. (2017). Detecting a structural change in functional time series using local Wilcoxon statisticspa
dc.relation.referencesD. Paindaveine & G. Van bever (2013). From Depth to Local Depth: A Focus on Centrality Journal of the American Statistical Associationspa
dc.relation.referencesS. Lopez-Pintado, R. Jornsten (2007). Functiodal analysis via extensions of the band depth J Stat Softw 29:5spa
dc.relation.referencesAGRESTI, A. Y FINLAY, B. , Statistical Methods for the Social Sciences, 4th ed. Prentice Hall, 2009.spa
dc.relation.referencesR CORE TEAM., R: A Language and Environment for Statistical Computing. Tech. rep. Vienna, Austria, 2020. url: https://www.R-project.orgspa
dc.relation.referencesSISTEMA NACIONAL DE INFORMACIÓN DE LA EDUCACIÓN SUPERIOR, SNIES. Minedu-cación. BOGOTA, 2000. url: https://www.mineducacion.gov.co/sistemasinfo/Informacion-Institucional/.spa
dc.relation.referencesAzzalini, A., with the collaboration of A. Capitanio (2014). The Skew-Normal and Related Families. Cambridge University Press, Cambridgespa
dc.relation.referencesKoenker, R., Bassett Jr., G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.spa
dc.relation.referencesMazucheli, J., Alves, B., Menezes, A. and Leiva, V. An overview on parametric quantile regression models and their computational implementation with applications to biomedical problems including COVID-19 data. Computer Methods and Programs in Biomedicine 2022, 221, 106816.spa
dc.relation.referencesMazucheli, J., Alves, B., Menezes, A. and Leiva, V. An overview on parametric quantile regression models and their computational implementation with applications to biomedical problems including COVID-19 data. Computer Methods and Programs in Biomedicine 2022, 221, 106816.spa
dc.relation.referencesMuzamhindo, Yusheng Kong and Takur iramunashe Famba. (2017); PRINCIPAL COMPONENT ANALYSIS AS A RANKING TOOL - A CASE OF WORLD UNIVERSITIES. int J. of Adv. Res. 5 (Jun). 2114-2135 (ISSN 2320-5407). www.journalijar.comspa
dc.relation.referencesBracken J, van Assen MALM. An empirical Kaiser criterion. Psychol Methods. (2017);22(3):450-466. doi: 10.1037/met0000074. Epub 2016 Mar 31. PMID: 27031883.spa
dc.relation.referencesR Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/spa
dc.relation.referencesAndrzej Mackiewicz, Waldemar Ratajczak. Principal components analysis (PCA) (1993), Computers & Geosciences, Volume 19, Issue 3,1993, Pages 303-342, ISSN 0098-3004, https://doi.org/10.1016/0098-3004(93)90090-R.spa
dc.relation.referencesBanco Mundial. Indicadores de Desarrollo Mundial, World Bank Group, 2015, data. worldbank.orgspa
dc.relation.referencesKhatun, Tahmina. (2009). Measuring environmental degradation by using principal component analysis. Environment, Development and Sustainability. 11. 439-457. 10.1007/s 10668- 007-9123-2.spa
dc.relation.referencesPERSSON, RICKARD, Weight of evidence transformation in credit scoring models: How does it affect the discriminatory power? Student Paper, 2021.spa
dc.relation.referencesZENG, G., A necessary condition for a good binning algorithm in credit scoring, 2014.spa
dc.relation.referencesBasu S, Santra S (2010). "A joint model for incomplete data in crossover trials." Journal of Statistical Planning and Inference, 140(10), 2839-2845.spa
dc.relation.referencesChard AN, Trinies V, Edmonds CJ, Sogore A, Freeman MC (2019). "The impact of water consumption on hydration and cognition among schoolchildren: Methods and results from a crossover trial in rural Mali." PloS one, 14(1), e0210568.spa
dc.relation.referencesCruz NA, Melo OO, Martinez CA (2023). "A correlation structure for the analysis of Gaussian and non-Gaussian responses in crossover experimental designs with repeated measures." Statistical Papers, pp. 1-28.spa
dc.relation.referencesDavis CS (2002). Statistical Methods for the Analysis of Repeated Measurements. Springer, San Diego. [6] Fleiss JL (1989). "A critique of recent research on the two-treatment crossover design.* Controlled clinical trials, 10(3), 237-243.spa
dc.relation.referencesGrayling MJ, Mander AP, Wason JM (2018). *Blinded and unblinded sample size reesti-mation in crossover trials balanced for period." Biometrical Journal, 60(5), 917-933.spa
dc.relation.referencesHardin JW, Hilbe J (2003). Generalized Estimating Equations, Chapman & Hall, Boca Raton.spa
dc.relation.referencesHarville DA (1997). Matrix algebra from a statistician's perspective, volume I. Springer, New York.spa
dc.relation.referencesHin LY, Wang YG (2009). "Working-correlation-structure identification in generalized estimating equations." Statistics in medicine, 28(4), 642-658.spa
dc.relation.referencesHinkelmann K, Kempthorne O (2005). Design and Analysis of Experiments, volume Volume 2 of Wiley series in probability and mathematical statistics. Applied probability and statistics. Wiley, New York. ISBN 9780471551775,0-471-55177-5,0471551783.spa
dc.relation.referencesHojsgaard S, Halekoh U, Yan J (2006). "The R package geepack for generalized estimating equations." Journal of statistical software, 15, 1-11.spa
dc.relation.referencesJones B, Kenward MG (2015). Design and Analysis of Cross-Over Trials Third Edition. Chapman & Hall/CRC, Boca Raton.spa
dc.relation.referencesJosephy H, Vansteelandt S, Vanderhasselt MA, Loeys T (2015). "Within-subject mediation analysis in AB/BA crossover designs." The international journal of biostatistics, 11(l), 1-22.spa
dc.relation.referencesKitchenham B, Madeyski L, Curtin F, et al. (2018). "Corrections to effect size variances for continuous outcomes of cross-over clinical trials." Statistics in medicine, 37(2), 320-323.spa
dc.relation.referencesKleinman K, Sakrejda A, Moyer J, Nugent J, Reich N (2021). clusterPower: Power Calculations for Cluster-Randomized and Cluster-Randomized Crossover Trials. R package version 0.7.0, URL ht tps:// CRAN.R-project.org/package=clusterPower.spa
dc.relation.referencesLabes D (2019). randomizeBE: Create a Random List for Crossover Studies. R package version 0.3-5, URL https:// CRAN.R-project. org/package=randomizeBEspa
dc.relation.referencesLiang KY, Zeger SL (1986). "Longitudinal data analysis using generalized linear models." Biometrika, 73(1), 13-22.spa
dc.relation.referencesMadeyski L, Kitchenham B (2018). "Effect sizes and their variance for AB/BA crossover design studies." Empirical Software Engineering, 23(4), 1982-2017.spa
dc.relation.referencesMcDaniel LS, Henderson NC, Rathouz PJ (2013). "Fast pure R implementation of GEE: application of the Matrix package." The R Journal, 5, 181-187. URL https://journal! I-project.org/archive/2013-1/mcdaniel-henderson-rathouz.pdfspa
dc.relation.referencesPan W (2001). "Akaike's information criterion in generalized estimating equations." Bio-metrics, 57, 120-125.spa
dc.relation.referencesPatterson HD (1951). "Change-Over Trials." Journal of the Royal Statistical Society. Series B (Methodological), 13, 256-271.spa
dc.relation.referencesRohmeyer K (2021). Crossover: Analysis and Search of Crossover Designs. R package version 0.1-20, URL attps://CRAN.R-projget.org/package-Crossover.spa
dc.relation.referencesRosenkranz GK (2015). "Analysis of cross-over studies with missing data." Statistical methods in medical research, 24(4), 420-433.spa
dc.relation.referencesSailer MO (2022), crossdes: Construction of Crossover Designs. R package version 1.1-2, URL https://CRAN.R-project.org/package=crossdes.spa
dc.relation.referencesSenn S (1992). "Is the 'simple carry-over'model useful?" Statistics in Medicine, 11(6), 715-726.spa
dc.relation.referencesVegas S, Apa C, Juristo N (2016). "Crossover designs in software engineering experiments: Benefits and perils." IEEE Transactions on Software Engineering, 42(2), 120-135.spa
dc.relation.referencesWei T, Simko V (2021). R package 'corrplot: Visualization of a Correlation Matrix. (Version 0.92), URL https://github. com/taiyun/corrplot.spa
dc.relation.referencesZhang H, Yu Q, Feng C, Gunzler D, Wu P, Tu X (2012). "A new look at the difference between the GEE and the GLMM when modeling longitudinal count responses." Journal of Applied Statistics, 39(9), 2067-2079.spa
dc.relation.referencesN. M. Laird and J. H. Ware, Random-Effects Models for Longitudinal Data, Biometrics, vol. 38, no. 4, pp. 963-974, 1982. URL: http://www.jstor.org/stable/2529876spa
dc.relation.referencesB. A. Chvatal et al., Development of Multilevel Models for Longitudinal Craniofacial Growth Prediction, Am. J. Orthod. Dentofacial Orthop., vol. 128, no. 1, pp. 45-56, 2005. DOI: 10.1016/j-ajodo.2004. 03.035spa
dc.relation.referencesJ. C. Correa and J. C. Salazar, Introducción a los Modelos Mixtos, 2016. URL: http: //www.bdigital.unal.edu.co/57330/spa
dc.relation.referencesE. W. Frees, Longitudinal and Panel Data: Analysis and Applications in the Social Sciences, Cambridge University Press, 2004. New York.spa
dc.relation.referencesA. Charpentier, Confidence Intervals and Credible Intervals, 2016. URL: http:// freakonometrics.hypotheses.org/18117 Consultado el 24 de julio de 2023.spa
dc.relation.referencesL. D. Jiménez, L. Villegas, G. Álvarez, and J. C. Salazar-Uribe, Modeling Facial Growth Data in 49 Untreated Colombian Mestizo Subjects During 18 Years Follow-up Using Linear Mixed Models, Am. J. Orthod. Dentofacial Orthop., In Press, 2019.spa
dc.relation.referencesR: A Language and Environment for Statistical Computing, R Core Team, R Foundation for Statistical Computing, 2023. URL: https://www.R-project.org/spa
dc.relation.referencesAZZALINI, A. A class of distributions which includes the normal ones. Scandinavian Journal of Statistics, 1985, 12, 171-178.spa
dc.relation.referencesGUO, B. AND WANG, B Control charts for monitoring the Weibull shape parameter based on type II censored sample. Quality and Reliability Engineering International, 2014spa
dc.relation.referencesLI, C.; SU, N.C.; SU, P.F. AND SHYR, Y. The design of X and R control charts for skew-normal distributed data. Communications in Statistics-Theory and Methods, 2014, 43(23), 4908-4924.spa
dc.relation.referencesMORALES, V.H. AND PANZA, C.A. Control charts for monitoring the mean of skew-normal samples. Symmetry, 2022, 14, 2302-2312.spa
dc.relation.referencesSHEN, X.; ZOU, C.; JIANG, W. AND TSUNG, F. Monitoring Poisson count data with probability control limits when sample sizes are time varying. Naval Research Logistics, 2013, 60(8), 625-636.spa
dc.relation.referencesCHOW S-C, SHAO J, WANG H, LOKHNYGINA Y., Sample size calculations in clinical research., In: Second. Chapman & Hall; 2008. p. 1-21spa
dc.relation.referencesFRIEDMAN LM, FURBERG CD, DEMETS DL, REBOUSSIN DM, GRANGER CB., Fundamentals of clinical trials. In: Fundamentals of Clinical Trials. Spinger; 2015. p. 1-17.spa
dc.relation.referencesKUMBHARE D, ALAVINIA M, FURLAN J., Hypothesis Testing in Superiority, Noninfe-riority, and Equivalence Clinical Trials. Am J Phys Med Rehabil. 2019;98(3):226-30. Interdisciplinary and Appliedspa
dc.relation.referencesCHARITOS T, DE WAAL PR Y VAN DER GAAG LC., Computing short-interval transition matrices of a discrete-time Markov chain from partially observed data. Stat Med. 2008 Mar 15;27(6): 905-21. doi: 10.1002/sim.2970. PMID: 17579926.spa
dc.relation.referencesALEXANDRE JACQUILLAT, A Queuing Model of Airport Congestion and Policy Implications at JFK and EWR Massachusetts Institute of Technology. 2012 May 11.spa
dc.relation.referencesHIGHAM, N.J. Y LIN, L., On pth Roots of Stochastic Matrices. Linear Algebra and its Applications, 435, 448-463,2011.spa
dc.relation.referencesHIGHAM, N.J., Functions Of Matrices. Society for Industrial and Applied Mathemat-ics,2008.spa
dc.relation.referencesMARCUS, M. AND MINC, H., ome Results on Doubly Stochastic Matrices. American Mathematical Society, 13(4), 571-579, 1962. doi: http://doi.org/10. 2307/2034828spa
dc.relation.referencesQ1-MING H. Y GUNN E., Note on the Stochastic Roots of Stochastic Matrices. Jourmal of Systems Science and Systems Engineering, 12(2), 210-223, Jun 2003.spa
dc.relation.referencesSara López-Pintado and Juan Romo, A half-region depth for functional data, Computational Statistics & Data Analysis, vol. 55, no. 4, pp. 1679-1695, 2011, Elsevier.spa
dc.relation.referencesJames O. Ramsay and Bernard W. Silverman, Applied functional data analysis: methods and case studies, 2002, Springer.spa
dc.relation.referencesSubhabrata Chakraborti and Marien Graham, Nonparametric statistical process control, 2019, John Wiley & Sons.spa
dc.relation.referencesPartha Sarathi Mukherjee, On phase Il monitoring of the probability distributions of univariate continuous processes, Statistical Papers, vol, 57, no. 2, pp. 539-562, 2016. Springer.spa
dc.relation.referencesDouglas C. Montgomery, Statistical quality control, vol. 7, 2009, Wiley New York.spa
dc.relation.referencesAlessandro Fasso, Maurizio Toccu, and Marino Magno, Functional control charts and health monitoring of steam sterilizers, Quality and Reliability Engineering International, vol. 32, no. 6, pp. 2081-2091, 2016, Wiley Online Library.spa
dc.relation.referencesAlba M Franco Pereira and Rosa E Lillo, Rank tests for functional data based on the epigraph, the hypograph and associated graphical representations, Advances in Data Analysis and Classification, vol. 14, no. 3, pp. 651-676, 2020, Springer.spa
dc.relation.referencesMiguel Flores, Salvador Naya, Rubén Fernández-Casal, Sonia Zaragoza, Paula Raña, and Javier Tarrfo-Saavedra, Constructing a control chart using functional data, Mathematics, vol. 8, no. 1, p. 58, 2020, MDPI.spa
dc.relation.referencesPiotr Kokoszka and Matthew Reimherr, Introduction to functional data analysis, 2017, CRC press.spa
dc.relation.referencesAna Arribas-Gil and Juan Romo, Shape outlier detection and visualization for functional data: the outliergram, Biostatistics, vol. 15, no. 4, pp. 603-619, 2014, Oxford University Press.spa
dc.relation.referencesSara López-Pintado and Juan Romo, On the concept of depth for functional data, Journal of the American statistical Association, vol. 104, no. 486, pp. 718-734, 2009, Taylor & Francis.spa
dc.relation.referencesChristian Capezza, Fabio Centofanti, Antonio Lepore, and Biagio Palumbo, A functional data analysis approach for the monitoring of ship CO 2 emissions, Gestão & Produção, vol. 28, 2021, SciELO Brasil.spa
dc.relation.referencesMarco Grasso, Bianca Maria Colosimo, and Fugee Tsung, A phase I multi-modelling ap- proach for profile monitoring of signal data, International Joumal of Production Research, vol. 55, no. 15, pp. 4354-4377, 2017, Taylor & Francis.spa
dc.relation.referencesKoenker, R.; Bassett, G., Jr. Regression quantiles. Econometrica 1978, 46, 33-50.spa
dc.relation.referencesMcKeague, I.W.; López-Pintado, S.; Hallin, M.; Siman, M. Analyzing growth trajectories. J. Dev. Orig. Health Dis. 2011 2, 322-329.spa
dc.relation.referencesPetrella, L.; Raponi, V. Joint estimation of conditional quantiles in multivariate linear regression models with an application to financial distress. J. Multivar. Anal. 2019, 173, 70-84.spa
dc.relation.referencesBARÓN, J. & MARTÍNEZ, G., Probabilistic Sampling Design and Strategies. Package ProbSamplingI, software R-project, 2021.spa
dc.relation.referencesSARNDAL, C., SWENSSON, B. & WRETMAN, J.. Model Assisted Survey Sampling, 2003.spa
dc.relation.referencesCalderón, S. (2023). TimeSeries. GitHub. de URL: https: //github.con/sacalderonv/ Time Seriesspa
dc.relation.referencesMartinez C. (2017) Suavizacion Exponencial Simple - Pronostico de la Demanda en Excel de https://excelcartagena. com.spa
dc.relation.referencesSciPy developers. (Agosto, 2023). SciPy Reference Guide. URL: https://docs. scipy org/doc/scipy/spa
dc.relation.referencesStatsmodels developers. (AMayo, 2023). User Guide. URL: https://www.statsmodels org/stable/user-guide.htmlspa
dc.relation.referencesBARDWELL, G. E. AND CROW, E. L., A two-parameter family of hyper-poisson distribu-tions. Journal of the American Statistical Association, 59(305): 133-141, (1964)...spa
dc.relation.referencesBENJAMIN, M. A., RIGBY, R. A., AND STASINOPOULOS, D. M., Generalized au-toregressive moving average models. Journal of the American Statistical Association, 98(461):214-223, (2003).spa
dc.relation.referencesJOHNSON, N., KEMP, A., AND KOTZ, S. Univariate Discrete Distributions. Wiley Series in Probability and Statistics. Wiley, (2005).spa
dc.relation.referencesR Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, (2022).spa
dc.relation.referencesSÁEZ-CASTILLO, A. AND CONDE-SÁNCHEZ, A. A hyper-poisson regression model for overdispersed and underdispersed count data. Computational Statistics & Data Analysis, 61:148-157, (2013).spa
dc.relation.referencesR, r-project.org, R version 4.2.3, 2023.spa
dc.relation.referencesZEITLIN, W. AND ES AUERBACH, C., Basic statistics for the behavioral and social sciences using R., Oxford University Press, 2019.spa
dc.relation.referencesGRAJALES-MORALES S., https://github.com/SaraGraMo/DaneFinancEducat,2023.spa
dc.relation.referencesHarrou, F., Sun, Y., Hering, A. S., & Madakyaru, M. Statistical process monitoring using advanced data-driven and deep learning approaches: theory and practical applications. Elsevier.2020spa
dc.relation.referencesNurettin Dorukhan Sergin & Hao Yan Toward a better monitoring statistic for profile monitoring via variational autoencoders, Joural of Quality Technology, 53:5, 454-473,2021spa
dc.relation.referencesLee, S., Kwak, M., Tsui, K. L., & Kim, S. B. Process monitoring using variational au-toencoder for high-dimensional nonlinear processes. Engineering Applications of Artificial Intelligence, 83, 13-27.2019spa
dc.relation.referencesBRIAN EVERITT, & HOTHORN, T., An Introduction to Applied Multivariate Analysis with R,Springer, 2011 ed.spa
dc.relation.referencesCATTELL, R. B. The scree test for the number of factors, "Multivariate Behavioural Re-search, 1, 245-276. Cited on p. 72,1966.spa
dc.relation.referencesKAISER, H., The varimax criterion for analytic rotation in factor analysis, " Psychometrika, 23, 187-200. Cited on p. 72, 146, 1958.spa
dc.relation.referencesHAN, J., PEL, J., & KAMBER, M., Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) (English Edition), 3.a ed, Morgan Kaufmann, 2011.spa
dc.relation.referencesHOERL, ARTHUR E AND KENNARD, ROBERT W, Ridge regression: Biased estimation for nonorthogonal problems. Technometries, Taylor & Francis, 1970.spa
dc.relation.referencesGOLUB, G.H. AND REINSCH, Singular value decomposition and least square solutions Numer. Math, Marcel Dekker, Inc., 1970.spa
dc.relation.referencesHOERL, A.E., Applications of ridge analysis to regression problems, Chem. Eng. Progress, 1962.spa
dc.relation.referencesBELSLEY, DA, E, Kuh, RE Welsch, Regression Diagnostics. New York: Wiley, 1980.spa
dc.relation.referencesR. Ferland, A. Latour, D. Oraichi, Integer-Valued GARCH Process, Journal of Time Series Analysis, vol. 27, no. 6, pp. 923-942, 2006. https: //doi. org/10.1111/j. 1467-9892 2006. 00496.20spa
dc.relation.referencesK. Fokianos, A. Rahbek, D. Tjestheim, Poisson Autoregression, Journal of the American Statistical Association, vol. 104, no. 488, pp. 1430-1439, 2009. http://www.jstor.org/ stable/ 40592351spa
dc.relation.referencesK. Fokianos, D. Tjostheim, Log-linear Poisson autoregression, Journal of Multivariate Analysis, vol. 102, no. 3, pp. 563-578, 2011. https://doi.org/10.1016/j. juva. 2010. 11.002spa
dc.relation.referencesK. Fokianos, Count Time Series Models, Time Series Analysis: Methods and Applications, 2012. http://dx.doi. org/10.1016/B978-0-444-53858-1. 00012-0spa
dc.relation.referencesA. Zhang, Z. C. Lipton, M. Li, A. J. Smola, Dive into Deep Learning, arXiv preprint arXiv:2106.11342, 2021. [6] S. Ruder, An overview of gradient descent optimization algorithms, CORR, vol. abs/1609.04747, 2016. http://arxiv.org/abs/1609.04747spa
dc.relation.referencesR. J. Hyndman, G. Athanasopoulus, Forecasting: Principles and Practice, Monash Univer-sity, Australia, 2018,spa
dc.relation.referencesT. Liboschik, K. Fokianos, R. Fried, scount: An R Package for Analysis of Count Time Series Following Generalized Linear Models, Journal of Statistical Software, vol. 82, no. 5, pp. 1-51, 2017. https://www.jstatsoft.org/index.php/jss/article/view/v082i05 https://doi.org/10.18637/jss.v082. 105spa
dc.relation.referencesBERRENDERO, J. R., JUSTEL, A., & SVARC, M., Principal components for multivariate functional data. Computational Statistics & Data Analysis., Recuperado de https: // repositorio.uam.es/bitstream/handle/10486, 2011.spa
dc.relation.referencesYAMAMOTO, M, Clustering of functional data in a low-dimensional subspace Advances in Data Analysis and Classification. Recuperado de https://link. springer) com/article/10.1007/s11634-012-0113-3, 2012.spa
dc.relation.referencesMedellincomovamos (2021), 2020 Pico de probreza en Medellin. https://www.medellincomovamos.org/2020-pico-de-la-pobreza-en-medellinspa
dc.relation.referencesBreadfortheworld, https://www.bread.org/es/que-causa-el-hambrespa
dc.relation.referencesAlcaldia de Medellin, Encuesta de calidad de vida 2020. https://www.medellin.gov.co/irj/portal/medellin?NavigationTarget=contenido/9946- Encuesta-de-Calidad-de-Vida-2020spa
dc.relation.referencesKass, R. E., Gilks, W. R., Richardson, S., Spiege lhalter, D. J. (1997), Markov Chain Monte Carlo in Practice. Journal of the American Statistical Association. https://doi.org/10.2307/2965438spa
dc.relation.referencesChang, M., Dalpatadu, R. J., Phanord, D., Singh, A. K., Harrah, W. F., Administration, H. (2018), Breast Cancer Prediction Using Bayesian Logistic Regression. https://doi.org/10.31031/OABB.2018.02.000537spa
dc.relation.referencesBox G E P and Tiao G C. (1992), Bayesian Inference in Statistical Analysis. Canada: John Wiley Sons, Incspa
dc.relation.referencesR package version 3-13, Bayesian graphical models using MCMC. http://cran.r-project.org/package=rjagsspa
dc.relation.referencesARAGÓN-MORENO, J. A., Y LERMA-LERMA, B. D., Analysis temporary space (1981-2010) of the precipitation in the city of Bogota: advances in the generation of extreme indices. Revista Facultad de Ingenieria, 28(57), 57-71. (2019).spa
dc.relation.referencesSOCHA, L. M., Análisis tendencial de la variación climática " temperatura y precipitación" espacio-temporal del departamento de Boyacá, (2014)spa
dc.relation.referencesWIKLE, C. K., ZAMMIT-MANGION, A., AND CRESSIE, N., Spatio-Temporal Statistics with R, Boca Raton (2019)spa
dc.relation.referencesMATEU, J. GIRALDO, R, Geostatistical Functional Data Analysis (2021)spa
dc.relation.referencesYuanjun Guo, Zhile Yang, Shengzhong Feng, Jinxing Hu, Complex power system status monitoring and evaluation using big data platform and machine learning algorithms: a review and a case study, Complexity, vol. 2018, 2018, Hindawi.spa
dc.relation.referencesDouglas C. Montgomery, Statistical quality control, vol. 7, 2009, Wiley New York.spa
dc.relation.referencesMohamed E. El-Hawary, Electrical Power Systems: Design and Analysis, vol. 7, 1995, TEBE Press.spa
dc.relation.referencesIván Matulic, Introducción a Los Sistemas eléctricos de potencia, Acta Nova, Universidad Católica Boliviana, 2005, http://www.scielo.org.bo/scielo.php?script-sci arttext&amp: pid=51683-07892003000100005.spa
dc.relation.referencesComisión de Regulación de Energía y Gas, Resolución CREG 025-95, Codigo de Redes, 1995.spa
dc.relation.referencesWalter Andrew Shewhart, Economic quality control of manufactured product 7, Bell System Technical Joumal, vol. 9, no. 2, 1930, Wiley Online Library.spa
dc.relation.referencesEwan S Page, Continuous inspection schemes, Biometrika, vol. 41, no. 1/2, 1954, JSTOR.spa
dc.relation.referencesSW Roberts, Control chart tests based on geometric moving averages, Technometrics, vol. 42, no. 1, 2000, Taylor & Francis.spa
dc.relation.referencesJA Vargas, Control estadístico de calidad, Universidad Nacional de Colombia, Bogotá, Colombia, 2006.spa
dc.relation.referencesS. Calderón, Notas de clase Series de tiempo, Universidad Nacional de Colombia, 2022.spa
dc.relation.referencesMARTINEZ-FLÓREZ, G., MORENO-ARENAS, G. Y VERGARA-CARDOZO, S. Properties and inference for proportional hazard models. Revista Colombiana de Estadistica, 2013, Vol. 36, 95-114.spa
dc.relation.referencesMARTÍNEZ-FLÓREZ, G., GÓMEZ, H. W. Y TOVAR-FALÓN, R. Modeling Proportion Data with Inflation by Using a Power-Skew-Normal/Logit Mixture Model. Mathematics 2021, 9, 1989.spa
dc.relation.referencesAGRESTI, A. (2002). Categorical Data Analysis. John Wiley and Sons. New Jersey.spa
dc.relation.referencesCHRISTENSEN, R. (1997). Log-Linear Models and Logistic Regression. Springer-Verlag. New York.spa
dc.relation.referencesMARTÍNEZ-FLÓREZ, G., MORENO-ARENAS, G. Y VERGARA-CARDOZO, S. (2013). Properties and inference for proportional hazard models. Revista Colombiana de Estadística. Vol. 36, 95-114.spa
dc.relation.referencesPAOLINO, P. Maximum likelihood estimation of models with beta-distributed dependent variables. Political Anal. 2001, 9, 325-346.spa
dc.relation.referencesCRIBARI-NETO, F. Y VASCONCELLOS, K. L. P. Nearly unbiased maximum likelhood estimation for the beta distribution. J. Stat. Comput. Simul. 2002, 72, 107-118.spa
dc.relation.referencesKIESCHNICK, R. Y MCCULLOUGH, B. D. Regression analysis of variates observed on (O, 1). Stat. Model. 2003, 3, 193-213.spa
dc.relation.referencesFERRARI, S.; CRIBARI-NETO, F. Beta regression for modelling rates and proportions. J. Appl. Stat. 2004, 31, 799-815.spa
dc.relation.referencesVASCONCELLOS, K. L.P.: CRIBARI-NETO, F. Improved maximum likelihood estimation in a new class of beta regression models. Braz. J. Probab. Stat 2005, 19, 13-31.spa
dc.relation.referencesMARTÍNEZ-FLÓREZ, G., BOLFARINE, H. Y GÓMEZ, H. W.. Skew-normal Alpha-Power Model. 2013 Statistics-doi 10.1080/02331888.2013.826659.spa
dc.relation.referencesCRAGG, J. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 1971, 39, 829-844.spa
dc.relation.referencesMOULTON, L. Y HALSEY, N. A Mixture Model with Detection Limits for Regression Analysses of Antibody Response to Vaccine. Biometrics, 1995, 51, 1550-1578.spa
dc.relation.referencesMARTÍNEZ-FLÓREZ, G., GOMEZ, H. W. Y TOVAR-FALON, R. Modeling Proportion Data with Inflation by Using a Power-Skew-Normal/Logit Mixture Model, Mathematics, 2021, 9, 1989.spa
dc.relation.referencesEL-MORSHEDY, M., ELIWA, M. S., & ALTUN, E., Discrete Burr-Hatke Distribution With Properties, Estimation Methods and Regression Model, IBEE Access, 2020.spa
dc.relation.referencesRIGBY, R.A. AND STASINOPOULOS, D.M., Generalized Additive Models for Location, Scale and Shape, Journal of the Royal Statistical Society: Series C (Applied Statistics), 2005.spa
dc.relation.referencesMultivariate functional random fields: prediction and optimal sampling. Stochastic Environmental Research and Risk Assessment. 2017 * Bohorquez, M., & Sandoval D., Villamil A., Guevara R., Sanchez S., Vergel N., Bejaranospa
dc.relation.referencesV., Castro J., Muñoz M., Giraldo R., Mateu J. SpatFD (Version 0.0.1). R cran package. https://github.com/mpbohorquezc/GeostatFunct. (2023).spa
dc.relation.referencesVillamizar, S., Lopez, O., Collazos, A. C., Sarmiento, J., and Rodriguez, J. B. Recognition of EEG signals from imagined vowels using deep leaming methods. Sensors. 2021.spa
dc.relation.referencesC. DAVIDSON. QUICKSTART, 2020., URL https://github.com/CamDavidsonPilon/lifetimes/blob/master/docs/Quickstart.md.spa
dc.relation.referencesP. FADER AND B. HARDIE., The gamma-gamma model of monetary value., 2013.spa
dc.relation.referencesP. FADER, B. HARDIE, AND K. LEE., Counting your customers, the easy way: An alternative to the pareto/nbd model. 24:275-284, 2005.spa
dc.relation.referencesD. MCCARTHY AND E. WADSWORTH. BUY *TIL YOU DIE - A WALKTHROUGH, 2014., URL https://cran. r-project.org/web/packages/BTYD/vignettes/BTYDwalkthrough.pdfspa
dc.relation.referencesH. ONNEN., Buy 'til you die: Predict customer lifetime value in python, 2021. URL https://towardsdatascience.com/buy-til-you-die-predict-customer-lifetime-value-in-python-9701bfd4ddc0.spa
dc.relation.referencesD. SCHMITTLEIN, D. MORRISON, AND R. COLOMBO., Counting your customers: Who are they and what will they do next? 33: 1-24, 1987.spa
dc.relation.referencesLeung, A., Zhang, H., & Zamar, R. H. (2016). Robust Regression Estimation and Inference in the Presence of Cellwise and Casewise Contamination. Computational Statistics & Data Analysis, 99, 1-11.spa
dc.relation.referencesFalk, M. (1997). On Mad and Comedians. Annals of the Institute of Statistical Mathematics, 49(4), 615-644. [3] Kunjunni, S. O., & Sajesh, T. A. (2020). S, Covariance. Communications in Statistics - Theory and Methods, 49(24), 6133-6138.spa
dc.relation.referencesTarr, G., Muller, S., & Weber, N. (2012). A Robust Scale Estimator Based on Pairwise Means. Journal of Nonparametric Statistics, 24(1), 187-199. [5] Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273-1283.spa
dc.relation.referencesPinzón, M. A., Ortiz, S., Holguín, H., Betancur, J. F., Cardona Arango, D., Laniado, H., ... Quiceno, J. (2021). Dexamethasone vs Methylprednisolone High Dose for Covid-19 Pneumonia. PLOS ONE, 16(5), 1-13.spa
dc.relation.referencesJobe, J. M., & Pokojovy, M. (2015). A Cluster-Based Outlier Detection Scheme for Multivariate Data. Journal of the American Statistical Association, 110(512), 1543-1551.spa
dc.relation.referencesRo, K., Zou, C., Wang, Z., & Yin, G. (2015). Outlier Detection for High-Dimensional Data. Biometrika, 102(3), 589-599.spa
dc.relation.referencesHubert, M., Rousseeuw, P. J., Vanpaemel, D., & Verdonck, T. (2015). The DetS and DetMM Estimators for Multivariate Location and Scatter. Computational Statistics & Data Analysis, 87, 64-75.spa
dc.relation.referencesBoudt, K., Rousseeuw, P. J., Vanduffel, S., & Verdonck, T. (2020). The Minimum Regularized Covariance Determinant Estimator. Statistics and Computing, 30, 113-128.spa
dc.relation.referencesKandanaarachchi, S., & Hyndman, R. J. (2021). Dimension Reduction for Outlier Detection Using DOBIN, Journal of Computational and Graphical Statistics, 30(1),spa
dc.relation.referencesNavarro-Esteban, P., & Cuesta-Albertos, J. A. (2021). High-Dimensional Outlier Detection Using Random Projections.spa
dc.relation.referencesPena, D., &e Prieto, F: 1. (2001), Multivariate Outlier Detection and Robust Covariance Matrix Estimation. Technometrics, 43(3), 286-300.spa
dc.relation.referencesMaronna, R. A., & Zamar, R. H. (2002). Robust Estimates of Location and Dispersion for High-Dimensional Datasets. Technometrics, 44(4), 307-317.spa
dc.relation.referencesPeña, D., & Prieto, F. J. (2007). Combining Random and Specific Directions for Outlier Detection and Robust Estimation in High-Dimensional Multivariate Data. Journal of Computational & Graphical Statistics, 16(1), 228-254.spa
dc.relation.referencesRousseeuw, P. J., & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212-223.spa
dc.relation.referencesDonoho, D. (1982). Breakdown Properties of Multivariate Location Estimators. Harvard University, Boston.spa
dc.relation.referencesRousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. Wiley.spa
dc.relation.referencesRousseeuw, P. J. (1984). Least Median of Squares Regression. Journal of the American Statistical Association, 79(388), 871-880.spa
dc.relation.referencesStahel, W. A. (1981). Robuste Schatzungen: Infinitesimale Optimalitat und Schatzungen von Kovarianzmatrizen. ETH, Zurich, Switzerland.spa
dc.relation.referencesSajesh, T. A., & Srinivasan, M. R. (2012). Outlier Detection for High Dimensional Data Using the Comedian Approach. Journal of Statistical Computation and Simulation, 82(5), 745-757.spa
dc.relation.referencesAtkinson, A. C. (1994). Fast Very Robust Methods for the Detection of Multiple Outliers. Journal of the American Statistical Association, 89(428), 1329-1339.spa
dc.relation.referencesFilzmoser, P. (2005). Identification of Multivariate Outliers: A Performance Study. Austrian Journal of Statistics, 34(2), 127-138.spa
dc.relation.referencesHubert, Mia, & Van der Veeken, Stephan (2008). Outlier Detection for Skewed Data. Journal of Chemometrics, 22(3-4), 235-246.spa
dc.relation.referencesMagnotti, John F., & Billor, Nedret (2014). Finding Multivariate Outliers in fMRI Time-Series Data. Computers in Biology and Medicine, 53, 115-124.spa
dc.relation.referencesRocke, David M. (1989). Robust Control Charts. Technometrics, 31(2), 173-184.spa
dc.relation.referencesDavis, Cali Manning, & Adams, Benjamin M. (2005). Robust Monitoring of Contaminated Data. Journal of Quality Technology, 37(2), 163-174.spa
dc.relation.referencesGaleano, P., Peña, D., & Tsay, R. (2006). Outlier Detection in Multivariate Time Series by Projection Pursuit. Journal of the American Statistical Association, 101(474), 654-669.spa
dc.relation.referencesMaronna, Ricardo A., Martin, R. Douglas, & Yohai, Victor J. (2006). Robust Statistics: Theory and Methods. Wiley.spa
dc.relation.referencesRocke, David M., & Woodruff, David L. (1996). Identification of Outliers in Multivariate Data. Journal of the American Statistical Association, 91(435), 1047-1061.spa
dc.relation.referencesBrys, G., Hubert, M., & Struyf, A. (2004). A Robust Mfasure of Skewness. Journal of Computational and Graphical Statistics, 13(4), 996-1017.spa
dc.relation.referencesOlive, D. J. (2004). A Resistant Estimator of Multivariate Location and Dispersion. Computational Statistics & Data Analysis, 46(I), 93-102.spa
dc.relation.referencesA gostinelli, C., Leung, A., Yohai, V. J., & Zamar, R. H. (2015). Robust Estimation of Multivariate Location and Scatter in the Presence of Cellwise and Casewise Contamination. TEST, 24(3), 441-461.spa
dc.relation.referencesCuesta-Albertos, J. A., & Nieto-Reyes, A. (2008). The Random Tukey Depth. Computational Statistics & Data Analysis, 52(11), 4979-4988.spa
dc.relation.referencesFraiman, Ricardo, Meloche, Jean, García, Luis A., Gordaliza, Alfonso, He, Xuming, Maronna, Ricardo,.. Wood, Andrew (1999). Multivariate L-estimation. TEST, 8(2), 255-317.spa
dc.relation.referencesLiu, Regina Y., & Parelius, Jesse M., & Singh, Kesar (1990). On a Notion of Data Depth Based on Random Simplices. The Annals of Statistics, 18(1), 405-414spa
dc.relation.referencesLiu, Regina Y., Parelius, Jesse M., & Singh, Kesar (1999). Multivariate Analysis by Data Depth: Descriptive Statistics, Graphics and Inference. The Annals of Statistics, 27(3), 783-858.spa
dc.relation.referencesZuo, Yijun, & Serfling, Robert (2000). General Notions of Statistical Depth Function. The Annals of Statistics, 28(2), 461-482.spa
dc.relation.referencesCabana, Elisa, Lillo, Rosa E., & Laniado, Henry (2019). Multivariate Outlier Detection Based on a Robust Mahalanobis Distance with Shrinkage Estimators. Statistical Papers, 60(2), 199-210.spa
dc.relation.referencesHubert, M., & Vandervieren, E. (2008). An Adjusted Boxplot for Skewed Distributions. Computational Statistics & Data Analysis, 52(12), 5186-5201.spa
dc.relation.referencesFilzmoser, P., Maronna, R., & Werner, M. (2008). Outlier Identification in High Dimensions. Computational Statistics & Data Analysis, 52(3), 1694-1711.spa
dc.relation.referencesCuesta-Albertos, J. A., & Nieto-Reyes, A. (2008). The Random Tukey Depth. Computational Statistics & Data Analysis, 52(11), 4979-4988.spa
dc.relation.referencesLoperfido, N. (2018). Skewness-Based Projection Pursuit: A Computational Approach. Computational Statistics & Data Analysis, 120, 42-57.spa
dc.relation.referencesOrtiz, S. (2019). Multivariate Outlier Detection and Robust Estimation Using Skewness and Projections. Master's thesis, Universidad EAFIT, Medellín, Colombia.spa
dc.relation.referencesMaronna, Ricardo A., & Yohai, Víctor J. (1995). The Behavior of the Stahel-Donoho Robust Multivariate Estimator. Journal of the American Statistical Association, 90(429), 330-341.spa
dc.relation.referencesVan Aelst, S., Vandervieren, E., & Willems, G. (2012). A Stahel-Donoho Estimator Based on Huberized Outlyingness. Computational Statistics & Data Analysis, 56(3), 531-542.spa
dc.relation.referencesGervini, Daniel (2002). The Influence Function of the Stahel-Donoho Estimator of Multivariate Location and Scatter. Statistics & Probability Letteks, 60(4), 425-435.spa
dc.relation.referencesJones, M. C., & Sibson, R. (1987). What is Projection Pursuit? Journal of the Royal Statistical Society: Series A (General), 750(L), 1-18.spa
dc.relation.referencesAlgallaf, F., Van Aelst, S., Yohai, V. J., & Zamar, R. H. (2009). Propagation of Outliers in Multivariate Data. The Annals of Statistics, 37(1), 311-331.spa
dc.relation.referencesR Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.spa
dc.relation.referencesVan Aelst, S. (2016). Stahel-Donoho Estimation for High-Dimensional Data. International Journal of Computer Mathematics, 93(4), 628-639.spa
dc.relation.referencesJuan, J., & Prieto, F. J. (1995). A Subsampling Method for the Computation of Multivariate Estimators with High Breakdown Point. Journal of Computational & Graphical Statistics, 4(4), 319-334.spa
dc.relation.referencesBANCOLDEX, Bancoldex,30 de 07 de 2018. Obtenido de Bancoldex: attps: //www.bancoldex.com/sabe-que-es-el-sistema-financiero-colombiano-1630.spa
dc.relation.referencesCOLOMBIA COMPETITIVA, Índice de Competitividad Global -ICG (Global Competitiveness Index Obtenido de http://www.colombiacompetd-global, 2019.spa
dc.relation.referencesR. C. TEAM, «R: A language and environment for statistical computing,» 2016. [En Inea]spa
dc.relation.referencesTorres, R., Montes E., Pérez, O & Andrade, R. (2012). Influencia del estado de madurez. sobre las propiedades visco elásticas de frutas tropicales (mango, papaya y plátano), Información Tecnológica, 23(5), 115-124.spa
dc.relation.referencesOrganización Mundial de la Salud (OMS). (2015). Plan de acción mundial sobre la resistencia a los antimicrobianos [Internet]. 68.* Asamblea Mundial de la Salud; del 18 al 26 de mayo del 2015; Ginebra. Disponible en: https://apps.who.int/gb/ebwha/pdf_files/WHA68/A68_20-sp.pdfspa
dc.relation.referencesOrganización Mundial de la Salud (OMS). (2019). Antimicrobianos de importancia crítica para la medicina humana, 6.* revisión [Critically important antimicrobials for human medicine, 6th revisión]. Ginebra. Licencia: CC BY-NC-SA 3.0 IGO.spa
dc.relation.referencesOrganización Mundial de la Salud (OMS), Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO) y Organización Mundial de Sanidad Animal (OIE). (2021). Encuesta tripartita de autoevaluación nacional sobre la RAM (TrACSS) 2020-2021. Disponible en: https://www.who.int/es/publications/m/item/ tripartite-amr-country-solf-assessnent-supy®y-(traces)-2020-2021spa
dc.relation.referencesPagès, J. (2004) Analyse Factorielle de Données Mix https://www.who.int/es/publications/m/ite 52, 93-111 m/tripartite-amr-country-self-assessment-survey-(tracss)-2020-2021spa
dc.relation.referencesR Core Team (2022). R: A language and environment Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/spa
dc.relation.referencesWorld Organisation for Animal Health (OIE). (2021). OIE Annual Report on Antimicrobial Agents Intended for Use in Animals. better understanding of the global situation. Fifth report.spa
dc.relation.referencesABONAZEL, M.R., EL-SAYED, S. M., Y SABER, O. M, , Performance of robust count regression estimators in the case of overdispersion, zero inflated, and outliers: simulation study and application to German health data, Communications in mathematical biology and neuroscience, Article ID 55, 2021, https://doi.org/10.28919/cmbn/5658.spa
dc.relation.referencesASWI, A., ASTUTI, S. A., Y SUDARMIN, S., Evaluating the performance of zero-inflated and hurdle Poisson models for modeling overdispersion in count data. Inferensi, Vol. 5 num. 1, 2022, https://doi.org/10.12962/j27213862.v511.12422.spa
dc.relation.referencesCAMPBELL, H., The consequences of checking for zero-inflation and overdispersion in the analysis of count data, Methods in Ecology and Evolution, Vol. 12 num. 4, pp. 665-680, 2021, https://doi.org/10.1111/2041-210x.13559.spa
dc.relation.referencesHILBE, J. M., Negative Binomial Regression (2a ed.), Cambridge University Press, 2012.spa
dc.relation.referencesHILBE, J. M. ,Modeling Count Data, Cambridge University Press, 2014.spa
dc.relation.referencesZUUR, A. F., IENO, E. N., WALKER, N., SAVELIEV, A. A., Y SMITH, G. M, Negative Binomial Regression (2a ed.) Mixed effects models and extensions in ecology with R, Springer, 2011.spa
dc.relation.referencesBos, M. (2016.), educación en America Latina y el Caribe: Diagnostico y Perspectiva. BID-OCDE.spa
dc.relation.referencesCosta M. Domingos A.(2019) promover o ensino da matematica num contexto de formacao profissional com STEM. Educacion Matematica, vol 31,num 1.spa
dc.relation.referencesWISE (2019). Emprendedoras STEM en América latina. BID-LAB-IAE. [4] Mody, C. (2015). Scientific practice and science education. Science Education, 99(6), 1026-1032.spa
dc.relation.referencesR Core Team. (2021). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.orgspa
dc.relation.referencesVertel et al. (2021). Grupo de Estadística y Modelamiento Matemático aplicado a calidad educativa adscrito a la universidad de Sucre.spa
dc.relation.referencesTorres, R., Montes E., Pérez, O & Andrade, R. (2012). Influencia del estado de madurez: sobre las propiedades visco-elásticas de frutas tropicales (mango, papaya y plátano), Información Tecnológica, 23(5), 115-124.spa
dc.relation.referencesPardo, C. & Del Campo, P. (2007). Combinación de métodos factoriales y de análisis de conglomerados en R: el paquete FactoClass. Rev. Colombiana de Estadística, 30 (2), 231-245.spa
dc.relation.referencesR Development Core Team. (2020). R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. Available at: http://www.R-project.orgspa
dc.relation.referencesChessel, D., Dufour, A., & Thioulouse, J. (2004), The ade4 Package-I: One table Methods, RNews 4(1), 5-10.spa
dc.relation.referencesR Core Team. (2021). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.orgspa
dc.relation.referencesVertel et al. (2021). Grupo de Estadística y Modelamiento Matemático aplicado a calidad educativa adscrito a la universidad de Sucre.spa
dc.relation.referencesBoyd, L. R., & Muggia, F. M. (2018). Carboplatin/Paclitaxel Induction in Ovarian Cancer: The Finer Points. Oncology (Williston Park, N.Y.), 32(8), 418-420, 422-424.spa
dc.relation.referencesCobo, M. J., López Herrera, A. G., Herrera Viedma, E., & Herrera, F. (2012). SciMAT: A new science mapping analysis software tool. Journal of the American Society for Information Science and Technology, 63(8), 1609-1630. https://doi.org/10.1002/asi.22688spa
dc.relation.referencesDamia, G., & Broggini, M. (2019). Platinum Resistance in Ovarian Cancer: Role of DNA Repair. Cancers, 11(1), 119. https://doi.org/10.3390/cancers11010119spa
dc.relation.referencesNorouzibarough, L., Sarookhani, M. R., & Sharifi, M. (2017). Molecular Mechanisms of Drug Resistance in Ovarian Cancer. Journal of Cellular Physiology, 233(6), 4546-4562. https://doi.org/10.1002/j cp.26289spa
dc.relation.referencesHARRISON, N., CHUDRY, F., WALLER, R. Y HATT, S., Towards a Typology of Debt Attitudes among Contemporary Young uk Undergraduates. Journal of Further and Higher Education, Vol 39(1), 85-107, 2015.spa
dc.relation.referencesHAULTAIN, S., KEMP, S. Y CHERNISHENCKO, O., The structure of attitudes to student debt. Journal of Economic Psychology, Vol 31, 322-330, 2010.spa
dc.relation.references] KAUR, J. Y ARORA, S., Indian students' attitude toward educational debt: scale development and validation. Quality Assurance in Education, Vol 27(4), 361-383, 2019.spa
dc.relation.referencesPARK, T., YUSUF, A.A. Y HADSALL, R.S. , Pharmacy students' attitudes toward debt. American Journal of Pharmaceutical Education, Vol 79(4), 52, 2015.spa
dc.relation.referencesCEPEDA-CUERVO, E., Modelagem da Variabilidade em Modelos Lineares Generalizados. Unpublished Ph.D. thesis, Mathematics Institute, Universidade Federal do Rio de Janeiro, 2001.spa
dc.relation.referencesCEPEDA-CUERVO E., jo? beta and uo? Beta-Binomial Regression Models. Revista Colombiana de Estadística, 46(1), 63-79, 2023.spa
dc.relation.referencesCEPEDA-CUERVO E., Beta regression models: joint mean and variance modeling. Journal of Statistical Theory and Practice 9, 134-145, 2015.spa
dc.relation.referencesPAOLINO, P., Maximum likelihood estimation of models with beta-distributed dependent variables. Political Analysis, 9(4), 325-346, 2001.spa
dc.relation.referencesS. FERRARI AND F. CRIBARI-NETO., Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31 (7) 799-815, 2004.spa
dc.relation.referencesQUINE, S., Achievement orientation of aboriginal and white Australian adolescents, Ph.d. thesis, Australian National University, Australia.spa
dc.relation.referencesOKUBO, A. Y LEVIN, S. A., Diffusion and Ecological Problems, Modern Perspectives. Interdisciplinary and Applied Mathematics, Springer, 2001.spa
dc.relation.referencesQUINTERO-SARMIENTO, A., AND CEPEDA-CUERVO, E., AND NUNEZ-ANTON, V. , Estimating infant mortality in Colombia: some overdispersion modelling approaches. Joumal of Applied Statistics, 39(5), 1011-1036, 2012.spa
dc.relation.referencesH. Zhou, L. Li, and H. Zhu, Tensor Regression with Applications in Neuroimaging Data Analysis, J. Amer. Statist. Assoc. Vol. 108 502 (2013), pp. 540-552.spa
dc.relation.referencesJ. Bobb, C. Crainiceanu, B. Caffo, D. Reich and J. Goldsmith, Penalized Funcitonal Regression, J. Comput. Graph. Statist. Vol. 20 4 (201 1), pp. 830-851.spa
dc.relation.referencesA. Chambolle, An algorithm for total variation minimization and application, Comput. Imaging Vision Vol. 20 1 (2014), pp. 89-97.spa
dc.relation.referencesC. LI, W. Yin, H. Jiang and Y. Zhang, An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization, Comput. Optim. Appl. Vol. 56 3 (2013), pp. 507-530.spa
dc.relation.referencesC. LI, W. Yin, H. Jiang and Y. Zhang, An Alternating Direction Method for Total Variation Denoising, Optim. Methods Softw. Vol. 30 3 (2015), pp. 594-615.spa
dc.relation.referencesY. Wang, J. Yang, W. Yin, and Y. Zhang, A new alternating minimization algorithm for totalvariation image reconstruction, SIAM J. Imaging Sci. Vol. 13 (2018), pp. 248-272.spa
dc.relation.referencesC. Chen, L. He, H. Li, and J. Huang, Fast Iteratively Reweighted Least Squares Algorithms for Analysis-Based Sparsity Reconstruction, Med. Image Anal. Vol. 49 1 (2018), pp. 141-151.spa
dc.relation.referencesS. Toshi, Applied Matrix and Tensor Variate Data Analysis, Springer, Tokyo, 2016. [9] PT. Reiss, and RT. Ogden, Fuctional Principal Component Regression and Functional Partial Least Squares, J. Amer. Statist. Assoc. Vol. 49 1 (2018), pp. 984-996.spa
dc.relation.referencesPT. Reiss and RT. Ogden, Fuctional generalized Linear Models with Images as Predictors, Biometrics Vol. 66 1 (2010), pp. 61-69.spa
dc.relation.referencesK.J. Worsley, Developments in Random Field Theory, Human Brain Function: Second Edition Vol. 2 (2004).spa
dc.relation.referencesN. Lazar, The Statistical Analysis of Functional MRI Data, Springer, New York, 2008.spa
dc.relation.referencesKokoszka P. and M. Reimher, Introduction to Functional Data Analysis, Chapman and Hall-CRC, New York, 2017.spa
dc.relation.referencesL. Horváth and P. Kokoszka, Inference for Functional Data with Applications, Springer, New York, 2012.spa
dc.relation.referencesX. Wang and H. Zhu, Generalized Scalar-on-Image Regression Models via Total Variation, J. Amer. Statist. Assoc. Vol. 112 519 (2017), pp. 1156-1168.spa
dc.relation.referencesH. Zhou, Matlab Tensor Reg Toolbox Version 1.0, Available online. Software available at https://hua-zhou.github.io/TensorReg/.spa
dc.relation.referencesJ. Zeng, W. Wang X. and Zhang, TRES: An R Package for Tensor Regression and Envelope Algorithms, Available online. Software available at https://cran.r-project.org/web/packages/TRES/TRES.spa
dc.relation.referencesI.A. Eckley and G.P. Nason, LS2W: Implementing the Locally Stationary 2D Wavelet Process Approach in R, J. Stat. Softw. Vol. 43 3 (2011), pp. 1-23.spa
dc.relation.referencesG. Nason, Wavelet Methods in Statistics with R, Springer, New York, 2011.spa
dc.relation.referencesJ. A. Clarkson and C. R. Adams, On definitions of bounded variation for functions of two variables, Transactions of the American Mathematical Society Vol. 35 4 (1933), pp. 824-854spa
dc.relation.referencesP. J. Basser and S. Pajevic, A normal distribution for tensor valued random variables: applications to diffusion tensor MRI, IBEE transactions on medical imaging vol. 22 7 (2003), pp. 785-794.spa
dc.relation.referencesM. Ohlson and M. R. Ahmad and D. Von Rosen, The multilinear normal distribution: Introduction and some basic properties, Joumal of Multivariate Analysis, vol 113 (2013), pp 37-47.spa
dc.relation.references] M. Ohlson and M. R. Ahmad and D. Von Rosen, Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition, PLoS One, vol 10 (2015).spa
dc.relation.referencesL. laliang and G. Ming and D. Ralph, Evaluatin' classification accuracy for modern learning approaches, Statistics in medicine, vol 38 13 (2019), pp 2477-2503.spa
dc.relation.referencesS. SumaiyaZ and S. Loraine and L. Jialiang and D. Ralph, Statistical Learning in Medical Research with Decision Threshold and Accuracy Evaluatio, Journal of Data Science, vol 19 4 (2021)spa
dc.relation.referencesP. Margaret Sullivan The statistical evaluation of medical tests for classification and prediction, Oxford University Press, USA, 2003.spa
dc.relation.referencesH. David J and A, Christoforos, When is the area under the receiver operating characteristic curve an appropriate measure of classifier performance?, Patter Recognition Letters, vol 34 5 (2013), pp 492-495.spa
dc.relation.referencesT. Alaa, Classification assessment methods, Pattern Recognition Letters, vol 17 1 (2021), pp 168-192.spa
dc.relation.referencesN. Christos ROC Analysis for Classification and Prediction in Practice, CRC Press, 2023.spa
dc.relation.referencesD. Yang and C. Martinez and L. Visuña and H. Khandhar and C. Bhatt and J. Carretero, Detection and analysis of COVID-19 in medical images using deep learning techniques, Scientific Reports, vol 11 5 (2021), pp 19638spa
dc.relation.referencesM. Puttagunta and S. Ravi, Medical image analysis based on deep leaming approach, Multimedia tools and applications, vol 80 (2021), pp 24365-24398.spa
dc.relation.referencesBranscum, A. J., Johnson, W. O., & Thurmond, M. C. (2007), Bayesian beta regression: applications to household expenditure data and genetic distance between foot-and-mouth disease viruses. Australian & New Zealand Journal of Statistics, 49(3), 287-301.spa
dc.relation.referencesDiaz, P. R. (2018). Regresión con proyecciones aleatorias para datos funcionales (Doctoral dissertation, Uniandes).spa
dc.relation.referencesEspinheira, P. L., Ferrari, S. L., & Cribari-Neto, F. (2008). On beta regression residuals. Journal of Applied Statistics, 35(4), 407-419.spa
dc.relation.referencesFerrari, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of applied statistics, 31(7), 799-815.spa
dc.relation.referencesGalvis, D. M., Bandyopadhyay, D., & Lachos, V. H. (2014). Augmented mixed beta regression models for periodontal proportion data. Statistics in medicine, 33(21), 3759-3771.spa
dc.relation.referencesSentürk, D., Dalrymple, L. S., & Nguyen, D. V. (2014). Functional linear models for zero-inflated count data with application to modeling hospitalizations in patients on dialysis. Statistics in medicine, 33(27), 4825-4840.spa
dc.relation.referencesWang, J. L., Chiou, J. M., & Muller, H. G. (2016). Functional data analysis. Annual Review of Statistics and its application, 3, 257-295.spa
dc.relation.referencesYS Amirkhalili, A Aghsami, and F Jolai. Comparison of Time Series ARIMA Model and Support Vector Regression. In: International Journal of Hybrid Information Technology, 13.1 (2020), pp. 7-18.spa
dc.relation.referencesYukun Bao, Tao Xiong, and Zhongyi Hu. Multi-step-ahead time series prediction using multiple-output support vector regression. In: Neurocomputing, 129 (2014), pp. 482-493.spa
dc.relation.referencesBarreto, H. (2012). El progreso de la Estadística y su utilidad en la evaluación del desarrollo. Papeles de Población, 18(73), 241-27 L.spa
dc.relation.referencesS. KALIRAMESH, V. CHELLADURAI, D. JAYAS, K. ALAGUSUNDARAM, N. WHITE, AND P. FIELDS, Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging, Journal of Stored Products Research, 52:107-111, 2013.spa
dc.relation.referencesF. MARTINELLI, R. SCALENGHE, S. DAVINO, S. PANNO, G. SCUDERI, P. RUISI, P. VILLA, D. STROPPIANA, M. BOSCHETTI, L. R. GOULART, ET AL., Advanced methods of plant disease detection. A review, Agronomy for Sustainable Development, 35(1): 1-25, 2015.spa
dc.relation.referencesA. RATNADASS, P. FERNANDES, J. AVELINO, AND R. HABIB, Plant species diversity for sustainable management of crop pests and diseases in agroecosystems: a review, A gronomy for Sustainable Development, 32(1):273-303, 2012.spa
dc.relation.referencesJ. ZHANG, R. PU, W. HUANG, L. YUAN, J. LUO, AND J. WANG, Using in-situ hyperspec-tral data for detecting and discriminating yellow rust disease from nutrient stresses, Field Crops Research, 134:165-174, 2012.spa
dc.relation.referencesCABALLERO, Y., GIRALDO, R., AND MATEU, J., A spatial randomness test based on the box-counting dimension, AStA Advances in Statistical Analysis, 106:499-524, 2022.spa
dc.relation.referencesFALCONER, K., Fractal Geometry: Mathematical Foundations and Applications, John Wiley & Sons, Chichester, 2nd edition, 2004.*spa
dc.relation.referencesSTOYAN, D. AND STOYAN, H., Fractals, Random Shapes and Point Fields: Methods of Geometrical Statistics, John Wiley & Sons, Chichester, 1994.spa
dc.relation.referencesKOLDA, T. G. & BADER, B. W., Tensor decompositions and applications, SIAM Review, 51(3), 455-500, 2009.spa
dc.relation.referencesGUO, W., KOTSIA, I. & PATRAS, I., Tensor learning for regression, IEEE Transactions on Image Processing, 21(2), 816-827, 2011.spa
dc.relation.referencesOUT, C., NIE, F., YI, D. & WU, Y., Eficient image classification via multiple rank regression, IEEE Transactions on Image Processing, 22(1), 340-352, 2012.spa
dc.relation.referencesHILBE, J. M. (2009)., Logistic regression models., Boca Raton: Chapman HalV/CRC.spa
dc.relation.referencesHOSMER JR., D., Y LaMESHOW, S. (2000)., Applied logistic regression (2nd ed.). New York: John Wiley Sons.spa
dc.relation.referencesPANDO, V., Y SAN MARTÍN, R. (2004)., Regresión logística multinomial., 18, 323-327.spa
dc.relation.referencesACKROYD, S., Part Il Critique and development, En S. FLEETWOOD & S. ACKROYD (EDS.), Critical realist applications in organisation and management studies (pp. 121-153), Routledge, Taylor & Francis Group, 2004.spa
dc.relation.referencesBHASKAR, R., A Realistic Theory of Science, Verso, 1975.spa
dc.relation.referencesBHASKAR, R., Plato etc.: The Problems of Philosophy and their Resolution, Verso, 1994,spa
dc.relation.referencesBHASKAR, R., A Realist Theory of Science, Taylor & Francis Group, 2008.spa
dc.relation.referencesBHASKAR, R., Plato Etc. Problems of Philosophy and their Resolution, Taylor & Francis Group, 2010.spa
dc.relation.referencesCARAYANNIS, E. G., BARTH, T. D. & CAMPBELL, D. F., The Quintuple Helic innovation model: global warming as a challenge and driver for innovation, Journal of Innovation and Entrepreneurship, 1(1), 2, 2012.spa
dc.relation.referencesCARAYANNIS, E. G. & CAMPBELL, D. F. J., Triple Helix, Quadruple Helix and Quintuple Helix and How Do Knowledge, Innovation and the Environment Relate To Each Other?, International Journal of Social Ecology and Sustainable Development, 1(1), 41-69, 2010.spa
dc.relation.referencesCARAYANNIS, E. G. & CAMPBELL, D. F. J., Open Innovation Diplomacy and a 21 st Century Fractal Research, Education and Innovation (FREIE) Ecosystem: Building on the Quadruple and Quintuple Helix Innovation Concepts and the "Mode 3" Knowledge Production System, Journal of the Knowledge Economy, 2(3), 327-372, 2011. https: //doi.org/10.1007/813132-011-0058-3spa
dc.relation.referencesCARMAGNOLA, FRANCESCA & OSBORNE, FRANCESCO & TORRE, ILARIA, Cross-systems identification of users in the Social Web. 1101 C. CHEN, N. N. XIONG, X. GUO AND J. REN, The System Identification and Prediction of the Social Earthquakes Burst in Human Society, IEEE Access, 8, 103848-103859, 2020. doi: 10.1109/ACCESS. 2020. 2999575spa
dc.relation.referencesFERNÁNDEZ-ESQUINAS, M. & PEREZ-YRUELA, M., Knowledge Transfer in Regional Innovation Systems: The effects of Socioeconomic Structure, En H. PINTO (BD.), Resilient Territories: Innovation and Creativity for New Modes of Regional Development (pp. 53-74), Cambridge Scholar Publishing, 2015.spa
dc.relation.referencesGÓMEZ, A., Aportes para la construcción de una política pública para la formación doctoral en Colombia, 2015.spa
dc.relation.referencesHAMM, R., KOPPER, J., JAGER, A., KARL, H., STROTEBECK, F. & WARNECKE, C., RegTrans - Zwischenbericht I Regionale Transfereffekte verschiedener Hochschultypen Analyse und Strategien für eine verbesserte Potenzialausschöpfung, 2012.spa
dc.relation.referencesH. -T. WAI, A. SCAGLIONE & A. LESHEM, The social system identification problem, 2015 54th IEEE Conference on Decision and Control (CDC), Osaka, Japan, pp. 406-411, 2015. doi: 10. 1109/CDC. 2015.7402234spa
dc.relation.referencesJAEGER, A. & KOPPER, J., Third mission potential in higher education: measuring the regional focus of different types of HEls, Jahrbuch Fur Regionalwissenschaft, 95-118, 2014.spa
dc.relation.referencesKASIANIUK, K., A system-cybernetic approach to the study of political power. In-troductory remarks, Kybernetes, 47(6), 1262-1276, 2018. https://doi.org/10.1108/ K-04-2017-0146|spa
dc.relation.referencesLLISTERRI, J. J.J. & PIETROBELLI, C., Los Sistemas Regionales de Innovación en América Latina, Banco Interamericano de Desarrollo, 2011.spa
dc.relation.referencesMELO, A., BECK, C. L., PENA, J. I. & PARE, P. E., Knowledge transfer from universities to regions as a network spreading process, 4th IEEE International Symposium on Systems Engineering, ISSE 2018 - Proceedings, 2018. https://doi.org/10. 1109/SysEng. 2018, 8644398spa
dc.relation.referencesUNAL-CID, Lineamientos de Política y Plan Operativo para la política de Fomento y Formación de Vocaciones en Ciencia Tecnología e Innovación: Producto Final, 2020.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-CompartirIgual 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/spa
dc.subject.ddc510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasspa
dc.subject.lembANALISIS NUMERICO-PROCESAMIENTO DE DATOS-CONGRESOS,CONFERENCIAS,ETC.spa
dc.subject.lembNumerical analysis - data processing congresseseng
dc.subject.lembANALISIS DE REGRESION-PROCESAMIENTO DE DATOSspa
dc.subject.lembRegression analysis - data processingeng
dc.subject.lembANALISIS DE REGRESION LOGISTICAspa
dc.subject.lembLogistic regression analysiseng
dc.subject.proposalEstadísticaspa
dc.subject.proposalModeladospa
dc.subject.proposalRegresiónspa
dc.subject.proposalAnálisis de datosspa
dc.subject.proposalSeries de tiempospa
dc.subject.proposalAprendizaje automático (Machine Learning)spa
dc.subject.proposalRedes neuronalesspa
dc.subject.proposalBayesianospa
dc.subject.proposalInferencia estadísticaspa
dc.subject.proposalControl estadístico de procesosspa
dc.subject.proposalDistribuciones estadísticasspa
dc.subject.proposalSimulaciónspa
dc.subject.proposalVisualización de datosspa
dc.title32° Simposio Internacional de estadística 2023 : bioestadística y datos funcionalesspa
dc.typeLibrospa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.driverinfo:eu-repo/semantics/bookspa
dc.type.redcolhttp://purl.org/redcol/resource_type/LIBspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
dcterms.audience.professionaldevelopmentMaestrosspa
dcterms.audience.professionaldevelopmentPúblico generalspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
Memorias_32_SIE_2023.pdf
Tamaño:
74.97 MB
Formato:
Adobe Portable Document Format
Descripción:
Memorias Simposio Internacional de Estadística N° 32
Descargar

Bloque de licencias

Mostrando 1 - 2 de 2
Miniatura
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción:
Descargar
Miniatura
Nombre:
Licencia para publicación de obras en el Repositorio Institucional UNAL_SIE 2023.pdf
Tamaño:
701.77 KB
Formato:
Adobe Portable Document Format
Descripción:
Descargar

Colecciones

Memorias del Simposio Internacional de Estadística