31° Simposio Internacional de estadística 2022 : múltiples temáticas

Vista sencilla de ítem
dc.contributor.conferencenameUniversidad Nacional de Colombia. Sede Bogotá. Facultad de Ciencias. Departamento de Estadística
dc.date.accessioned2025-02-14T16:00:29Z
dc.date.available2025-02-14T16:00:29Z
dc.date.issued2022
dc.descriptionilustraciones, diagramas, mapas, tablas
dc.description.abstractEl Simposio de Estadística de la Universidad Nacional de Colombia se empieza a realizar desde el 1990 en donde cuyo tema central fue “Análisis de regresión”. Desde ese momento existe el compromiso para realizar el encuentro anual en el que la comunidad científica, académica y en general cualquier persona interesada, acuden a un espacio de dialogo y de interacción, donde se actualizan los diferentes saberes del quehacer estadístico nacional, por medio de cursillos, conferencias, comunicaciones orales y pósteres, que se utilizan en el desarrollo de herramientas estadísticas en América Latina y facilitan la cooperación regional e internacional. En sus 31 años del Simposio se ha venido realizando en diferentes ciudades, y en colaboración conjunta con diferentes instituciones, llegando así a convertirse en un evento internacional, de esta manera y con los posibles obstáculos para todos después de casi dos años de emergencia sanitaria y después de una versión virtual, volvimos a la presencialidad. En ese sentido, con constancia, arduo trabajo y la mejor dedicación, en el año 2022 para la versión 31° de este evento, se escogió la ciudad de Manizales, y más específicamente la Universidad Nacional de Colombia en su sede la Nubia para la realización del evento. Múltiples Temáticas fueron propuestas para este año, con 188 participantes quienes pudieron interactuar en un entorno académico, con 9 conferencias magistrales, 7 cursillos, 45 póster, y 50 comunicaciones cortas. Destacamos la participación de los invitados internacionales Pierre Ribereau, de la Universidad Montpellier II, Ehyter Martín González, de la Universidad de Guanajuato, Paola Corrales, instructora y entrenadora de The Carpentries, de manera presencial. Rangga Handika, Profesor asociado en el Instituto de Estrategia Internacional de Tokio TIU, Gareth W. Peters, Profesor de la Universidad de Santa Bárbara (UCSB), Min Lu, Profesora de la Universidad de Miami, a través de videoconferencia, y el profesor Erwin Suazo en representación de la Universidad de Texas, Rio Grande Valley. Estas participaciones ponen al Simposio como referente Internacional y exaltan la importancia de la estadística a nivel nacional y regional. (Texto tomado de la fuente)spa
dc.description.edition31 ediciónspa
dc.description.tableofcontentsCOMUNICACIONES CORTAS -- 1. A modified version of the Stahel-Donoho multivariate outlier detection method based on specific random directions -- 2. Algoritmo para estimación de datos faltantes basado en regresiones lineales iteradas y penalizadas. -- 3. Análisis de clasificación para detección temprana del hongo Fusarium oxysporum en plantas de Banano Gros Michel con base en datos espectroscópicos VIS/NIR. -- 4. Análisis de datos Pseudo-Panel aplicado a retornos a la educación en Bogotá D.C -- 5. Análisis multivariado para identificar las barreras de género que enfrentan los aspirantes a la Universidad Nacional de Colombia. -- 6. Análisis y caracterización de variables influyentes en las brechas de género en el proceso de admisión de la Universidad Nacional de Colombia. -- 7. Classification in Point Patterns on Linear Networks Under Clutter. -- 8. Comparison of two control charts for monitoring skew - normal distributed data. -- 9. Comparación de pruebas de normalidad multivariada con muestras pequeñas. -- 10. Comparison of two control charts for monitoring increases in the Gumbel scale parameter when a Type- II censoring mechanism is operating. -- 11. Detección de cambio en la función de intensidad acumulada de un proceso TRP. -- 12. Diagnóstico de sesgos en el razonamiento probabilístico asociados a procesos de toma de decisiones en el contexto de una mini lotería. -- 13. Diseño de un cuestionario para conocer preferencias de consumidores de polen. -- 14. Diseño Experimental Consensual: Propuesta para incrementar las condiciones experimentales. -- 15. El Método Forest-Genetic para la estimación del umbral de daño fisiológico de Aeneolamia varia (Fabricius) en cultivos de caña de azúcar. -- 16. Elicitation of the Parameters of a Hierarchical Prior Distribution for the Parameter of a Poisson. -- 17. Estudio de la caracterización socioeconómica y demográfica de las personas afiliadas al régimen subsidiado de salud en Colombia, años 2019 y 2020. -- 18. Estudio de las propiedades de primer y segundo orden para patrones de líneas espaciales. -- 19. Estudio desagregado por sexo del recurso humano para Ciencia, Tecnología e Innovación (CTeI) en Colombia utilizando tablas de contingencia estructurada. -- 20. Evaluación de métodos estadísticos para la identificación de procedencias del café a partir de huellas espectrales. -- 21. Evaluación de una versión modificada de la prueba Shapiro-Wilk Generalizada con estimación shrinkage de la matriz de covarianzas. -- 22. Exogenous factors that determine the frequency of fatalities in road accidents in Colombian municipalities. -- 23. Explorando la presencia de sesgos en el razonamiento probabilístico condicional. -- 24. Hacia una visión pragmática de medición en Psicología. -- 25. Homicidios de líderes sociales, defensores de DD. HH y firmantes del acuerdo de paz en Colombia 2020-2022 – Un primer modelo Espacial. -- 26. Modelos psicométricos en la medición de variables no observables: El caso de la prueba ICSS 2016. -- 27. Modelamiento semiparamétrico de efectos carryover en diseños crossover con medidas repetidas. -- 28. Modelo de predicción para la vida útil restante de la batería de un vehículo eléctrico a partir de redes CNN-LSTM. -- 29. Modelo de prevención de fraude basado en video. Una aplicación de redes neuronales y modelos estadísticos. -- 30. Modelo de Scoring para la segmentación de asociados en una entidad de economía solidaria con baja tasa de default. -- 31. Mortalidad en Colombia por departamentos. -- 32. Non-stationary spatio-temporal point process modeling of COVID-19 data in Cali-Colombia. -- 33. Prueba Kruskal Wallis para Datos Funcionales. -- 34. Selección de variables en un modelo de regresión funcional cuantílico usando el método de regularización Lasso bayesiano agrupado. -- 35. Sensitivity Analysis for a numerical weather prediction model via an Ensemble Perspective. -- 36. Series Forecasting for the TransMilenio Bus Rapid Transit System -- 37. Statistical actuarial estimation of the Capitation Payment Unit from copula functions and deep learning: historical comparability analysis for the Colombian health system, 2015-2021. -- 38. Use of elicitation and social cartography to determine the importance and feasibility of landscape restoration areas in Belmira, Antioquia. -- 39. Uso de un modelo lineal generalizado hurdle para estimar conteos parciales latentes cuando solo los conteos totales están disponibles. -- 40. En Memoria de Julio Singer. -- PÓSTER -- 1. Análisis de la intensidad de discontinuidades en taludes rocosos a través de geometría estocástica -- 2. Análisis de la violencia de género en la ciudad de Medellín, ajustando un modelo de regresión logístico multinomial con un enfoque bayesiano -- 3. Análisis de los pronósticos en un modelo APC con enfoque bayesiano para la mortalidad por tuberculosis en Colombia según la agregación del periodo -- 4. Análisis de Públicos Digitales en la Red Cultural del Banco de la República usando modelos basados en BERT. -- 5. Análisis descriptivo y exploratorio de los estudiantes en carreras STEM de la UNAL Sede Bogotá para la detección de factores significativos en la deserción. -- 6. Análisis estadístico para el monitoreo epidemiológico del COVID-19: Caso Colombia. -- 7. Análisis espacial de la incidencia de la inseguridad sobre el precio del metro cuadrado de terreno en la ciudad de Bogotá para el año 2017. -- 8. Cargos por el uso de propiedad intelectual, pagos (Balanza de pagos US) en el periodo comprendido entre los años 1968-2021 en Colombia. -- 9. Clasificación y Rankeo de documentos en arXiv. -- 10. Comparación de algunos estimadores de la función de supervivencia bajo distintas tasas de censura. -- 11. Comparación entre el ajuste obtenido utilizando un modelo lineal de efectos mixtos y las metodologías RE-EM tree y random forest RMRF para la predicción de las ventas mensuales de Bavaria en el regional centro del país. -- 12. Estudio con enfoque bayesiano de la mortalidad conocidas características médicas de pacientes con COVID -19 hospitalizados en Georgia Atlanta. -- 13. Evaluación de los límites de control de Shewhart en la implementación de la Fase I bajo la distribución Gamma. -- 14. Gráficas sobre el avance del SARS-CoV-2 a nivel mundial en un trimestre. -- 15. Implementación de estrategias STEAM para el turismo estadístico-matemático de escolares de básica primaria en Sincelejo-Sucre, Colombia. -- 16. Implementación de métricas efectivas en analisis de datos. -- 17. Implementation of machine learning algorithms using robust and non-parametric techniques to recognize the infection level in coffee crops caused by Hemileia vastatrix fungus. -- 18. Modelación longitudinal de casos de dengue en Colombia, mediante modelos de conteo Poisson y ZIP de efectos Mixtos. -- 19. Modelamiento de tópicos aplicado al análisis de contenido de los tweets sobre el dengue en Colombia. -- 20. Modelo bayesiano basado en splines para predecir la mortalidad en algunos países de África a partir de información de la Demographic and Health Surveys (DHS). -- 21. “Modelos conjugados normales” una aplicación Shiny para la enseñanza de la estadística Bayesiana. -- 22. Modelo de regresión logístico bayesiano aplicado a casos de niños menores de 5 años con desnutrición aguda en la ciudad de Medellín. -- 23. Modelo espacial para determinar comorbilidades y factores sociodemográficos asociados al Covid-19. -- 24. Modelos tradicionales VS Modelos de aprendizaje de máquina: caracterización, alertas y recomendaciones de uso. -- 25. Nonparametric Approach for the Interaction in Two-way Factorial Designs: an review -- 26. Pronósticos en series de tiempo no lineales: aplicación del modelo TSAR y comparación con modelos para datos estacionales. -- 27. Propuesta para la construcción de un modelo de scoring crediticio utilizando las técnicas de regresión logística y bosque aleatorio para una entidad financiera -- 28. R vs Python para semilleros. Un ejemplo de análisis de sentimientos en twits sobre elecciones en Colombia 2022. -- 29. Spatial modeling of incidence~and~mortality~childhood~leukemia based on Colombian armed conflict and poverty for children born during the years 2002-2013. -- 30. Sample sizes for the application of the Central Limit Theorem in Poisson distributions with small population means. -- 31. Una modificación del BIC en Modelos Lineales.
dc.format.extent480 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/87492
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombia, Sede Bogotá, Facultad de Ciencias. Departamento de Cienciasspa
dc.publisher.placeBogotá, Colombiaspa
dc.relation.referencesAlqallaf, F., Van Aelst, S., Yohai, V. J., and Zamar, R. H. (2009), “Propagation of Outliers in Multivariate Data,” The Annals of Statistics, 37, 311 – 331.spa
dc.relation.referencesCuesta-Albertos, J. A. and Nieto-Reyes, A. (2008), “The Random Tukey Depth,” Computational Statis- tics & Data Analysis, 52, 4979 – 4988spa
dc.relation.referencesDonoho, D. (1982), “Breakdown Properties of Multivariate Location Estimators,” Technical report, Har- vard University, Boston.spa
dc.relation.referencesGervini, D. (2002), “The Influence Function of the Stahel-Donoho Estimator of Multivariate Location and Scatter,” Statistics & Probability Letters, 60, 425 – 435.spa
dc.relation.referencesHubert, M. and Van der Veeken, S. (2008), “Outlier Detection for Skewed Data,” Journal of Chemomet- rics, 22, 235 – 246.spa
dc.relation.referencesJuan, J. and Prieto, F. J. (1995), “A Subsampling Method for the Computation of Multivariate Estimators with High Breakdown Point,” Journal of Computational & Graphical Statistics, 4, 319 – 334.spa
dc.relation.referencesLoperfido, N. (2018), “Skewness-Based Projection Pursuit: A Computational Approach,” Computational Statistics & Data Analysis, 120, 42 – 57.spa
dc.relation.referencesMaronna, R. A. and Yohai, V. J. (1995), “The Behavior of the Stahel-Donoho Robust Multivariate Esti- mator,” Journal of the American Statistical Association, 90, 330 – 341.spa
dc.relation.referencesMaronna, R. A. and Zamar, R. H. (2002), “Robust Estimates of Location and Dispersion for High- Dimensional Datasets,” Technometrics, 44, 307 – 317.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.referencesPeña, D. and Prieto, F. J. (2001), “Multivariate Outlier Detection and Robust Covariance Matrix Estima- tion,” Technometrics, 43, 286 – 300.spa
dc.relation.references— (2007), “Combining Random and Specific Directions for Outlier Detection and Robust Estimation in High-Dimensional Multivariate Data,” Journal of Computational & Graphical Statistics, 16, 228 – 254.spa
dc.relation.referencesR Core Team (2022), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austriaspa
dc.relation.referencesRousseeuw, P. J. and Croux, C. (1993), “Alternatives to the Median Absolute Deviation,” Journal of the American Statistical Association, 88, 1273 – 1283spa
dc.relation.referencesStahel, W. A. (1981), Robuste Schatzungen: Infinitesimale Optimalitat und Schatzungen von Kovarianz- matrizen, Ph.D. thesis, ETH, Zurich, Switzerland.spa
dc.relation.referencesVan Aelst, S. (2016), “Stahel–Donoho Estimation for High-Dimensional Data,” International Journal of Computer Mathematics, 93, 628 – 639.spa
dc.relation.referencesVan Aelst, S., Vandervieren, E., and Willems, G. (2012), “A Stahel–Donoho Estimator Based on Huber- ized Outlyingness,” Computational Statistics & Data Analysis, 56, 531 – 542.spa
dc.relation.referencesHandbook ofMissingDataMethodology.EditedbyGeertMolenberghsetal.TaylorFran- cis Group,LLC.2015.spa
dc.relation.referencesLITTLE, R.;RUBIN, D.; Statistical AnalysiswithMissingData. Second Edition.Wilie Series inprobabilityandstatistics,2002spa
dc.relation.referencesMCKNIGHT, P.,MCKNIGHT, K.,SIDANI.,S.,FIGUEREDO, A. Mising Data-AGentle Introduction., 2007.spa
dc.relation.referencesEstimación dedatosfaltantesconelAlgoritmoEM.TesisparaobtenereltítulodeActuario. M. F.Lerdo.UniversidadAutónomadeMéxico,2014.spa
dc.relation.referencesJ.ABDULRIDHA, A. DE CASTRO, R.EHSANI , DifferentiateLaurelwiltdiseaseandnu- trient deficiencyinavocadotreesusingVis–NIRspectroscopy,in:Proceedingsofthe2015 ASABE AnnualInternationalMeeting NewOrleans,LA,USA,2015spa
dc.relation.referencesN.ABU-KHALAF, Sensing tomato’spathogenusingVisible/Nearinfrared(VIS/NIR)spec- troscopyandmultivariatedataanalysis(MVDA) Palest.Tech.Univ.Res.J.3(1)(2015) 12–22.spa
dc.relation.referencesZ.ARIVAZHAGAN, R.N.SHEBIAH, S.ANANTHI, S.V.VARTHINI, Detection ofunhealthy regionofplantleavesandclassificationofplantleafdiseasesusingtexturefeatures Agric. Eng. Int.CIGRJ.15(1)(2013)211–217.spa
dc.relation.referencesN.H.BROGE, E.LEBLANC, Comparing predictionpowerandstabilityofbroadbandand hyperspectralvegetationindicesforestimationofgreenleafareaindexandcanopychlo- rophylldensity Remote Sens.Environ.76(2)(2001)156–172spa
dc.relation.referencesA.CHEMURA, O.MUTANGA, T.DUBE, Remote sensingleafwaterstressincoffee(Coffea arabica)usingsecondaryeffectsofwaterabsorptionandrandomforests Phys.Chem.Earth PartsA/B/C.100(2017)317–324spa
dc.relation.referencesK.S.LING, T.TIAN, S.GURUNG, R.SALATI, A.GILLIARD, Firstreportoftomatobrown rugose fruitvirusinfectinggreenhousetomatointheUnitedStates Plant Dis.103(6)(2019) 1439spa
dc.relation.referencesA. Deaton.Paneldatafromtimeseriesofcross-sections. Journalofeconometrics., 1985.spa
dc.relation.referencesE CohnandJT.Addison.Theeconomicreturnstolifelonglearninginoecdcountries. Education economics, 1998.spa
dc.relation.referencesJ Mincer.Schooling,experience,andearnings.humanbehaviorsocialinstitutions. National BureauofEconomicResearch, 1974.spa
dc.relation.referencesD Card.Thecausaleffectofeducationonearnings. Handbook oflaboreconomics, 1999.spa
dc.relation.referencesO Ashenfelter,CHarmon,andHOosterbeek.Areviewofestimatesoftheschoo- ling/earnings relationship,withtestsforpublicationbias. Labour economics., 1999.spa
dc.relation.referencesS WarunsiriandRMcNown.Thereturnstoeducationinthailand:Apseudo-panelap- proach. WorldDevelopment, 2010.spa
dc.relation.referencesDANE.Colombia-granencuestaintegradadehogares-geih-2021. https://www.dane.gov.co, 2022.spa
dc.relation.referencesM VerbeekandTNijman.MinimumMSEestimationofaregressionmodelwithfixed effectsfromaseriesofcross-sections. JournalofEconometric, 1993.spa
dc.relation.referencesR. Moffitt.Identificationandestimationofdynamicmodelswithatimeseriesofrepeated cross-sections. 1993.spa
dc.relation.referencesM. D.Collado.Estimatingdynamicmodelsfromtimeseriesofindependentcross- sections. JournalofEconometrics, 1997spa
dc.relation.referencesY Mundlak.Onthepoolingoftimeseriesandcrosssectiondata. Econometrica: journal of theEconometricSociety, 1978.spa
dc.relation.referencesM VerbeekandTNijman.Cancohortdatabetreatedasgenuinepaneldata?.inpaneldata analysis. In Paneldataanalysis, 1992spa
dc.relation.referencesW.A.Fuller.Measurementerrormodels. JohnWileySons., pages103–121,1987.spa
dc.relation.referencesM. Guillerm.Pseudo-panelmethodsandanexampleofapplicationtohouseholdwealth data. Economie etStatistique, 2017.spa
dc.relation.referencesP KSenandJMSinger.Largesamplemethodsinstatistics:anintroductionwithappli- cations. CRC press., 1994.spa
dc.relation.referencesC Hsiao.Analysisofpaneldata. Cambridgeuniversitypress., pages28–52,2014.spa
dc.relation.referencesM Verbeek.Pseudo-panelsandrepeatedcross-sections. The econometricsofpaneldata, 2008.spa
dc.relation.referencesP.J.Devereux.Improvederrors-in-variablesestimatorsforgroupeddata. Journalof Business &EconomicStatistics, 2007.spa
dc.relation.referencesJ AngristandGImbens.Jackknifeinstrumentalvariablesestimation. JournalofApplied Econometrics, 1999.spa
dc.relation.referencesG Kapetanios.Abootstrapprocedureforpaneldatasetswithmanycross-sectionalunits. The EconometricsJournal, 2008spa
dc.relation.referencesTenjo,OÁlvarez,AGaviriaJaramillo,andMCJiménez.Evolutionofreturnstoedu- cation incolombia(19762014).2017spa
dc.relation.referencesROSADA GONZALES, OA.(2021).PropuestametodológicaparaelAjustedeunaRedde Fracturas Discretas(DFN)apartirdefotogrametríadecortoalcance.TesisdeMaestría, UniversidadNacionaldeColombiaspa
dc.relation.referencesBOHÓRQUEZ, M.(2020).EstadísticaEspacialyEspacio-temporalparacamposaleatorios escalares yfuncionales.Notasdeclase.UniversidadNacionaldeColombia.spa
dc.relation.referencesBADDELEY, A.,RUBAK, E.,TURNER, R.,2015.Spatialpointpatterns:methodologyand applications withR.CRCpressspa
dc.relation.referencesDavidJ.HandandRobertJ.Till(2001).ASimpleGeneralisationoftheAreaUnderthe ROCCurveforMultipleClassClassificationProblemsspa
dc.relation.referencesGregorZens,S.F.-S.(2021).EfficientBayesianModelingofBinaryandCategoricalData in R:TheUPGPackage.spa
dc.relation.referencesOrtizCalle,M.E.(2013).Violenciadegénero.NuevoDerecho,9(12),57-68.spa
dc.relation.referencesSalud,I.n.(2019).Vigilanciaenlasaludpublicadelasviolenciadegeneroeintrafamiliar, Colombia. Colombia.spa
dc.relation.referencesVelez-Gomez,P.,Restrepo-Ochoa,D.A.,Berbesi-Fernandez,D.,Trejos-Castillo,E. (2013). Depressionandneighborhoodviolenceamongchildrenandearlyadolescentsin Medellin, Colombia.Thespanishjournalofpsychology,16spa
dc.relation.referencesBray,I. (1995). ApplicationofMarkovChainMonteCarloMethodstoProjectingCancerIn- cidence andMortality.InournaloftheRoyalStatisticalSociety.(SeriesC(AppliedSta- tistics), 51(2),151–164.).http://www.jstor.org/stable/3592744spa
dc.relation.referencesPlummer,M. (2003). JAGS:AprogramforanalysisofBayesiangraphicalmodelsusingGibbs samplingspa
dc.relation.referencesMaartenGrootendorst.Bertopic:Neuraltopicmodelingwithaclass-basedtf-idfprocedure,2022.spa
dc.relation.referencesNilsReimersandIrynaGurevych.Makingmonolingualsentenceembeddingsmultilingualusingknowledge distillation. In Proceedingsofthe2020ConferenceonEmpiricalMethodsinNaturalLanguageProcessing. Association forComputationalLinguistics,112020.spa
dc.relation.referencesEstadística Descriptiva Multivariada (primera edición ed., Vol. 1). (2020). Universidad nacional de Colombiaspa
dc.relation.referencesJames, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. Springer Publishing.spa
dc.relation.referencesKassambara, A. (2017). Practical Guide To Principal Component Methods in R (Multi- variate Analysis) (Volume 2) (1.a ed.). CreateSpace Independent Publishing Platform.spa
dc.relation.referencesMontgomery, D. C., Peck, E. A., & Vining, G. G. (2012). Introduction to Linear Regres- sion Analysis: 821 (5th ed.). Wiley.spa
dc.relation.referencesLA DESERCIÓN ESTUDIANTIL DE LA UNIVERSIDAD NACIONAL DE COLOM- BIA SEDE MEDELLÍN EN EL SISTEMA DE EDUCACIÓN SUPERIOR COLOMBIANO. Universidad Nacional de Colombia Sede Medellín. (2011, noviembre). https://planeacion.medellin.unal.edu.co/images/documentos/DesercionComparada N-2011.pdfspa
dc.relation.referencesModelo de análisis de la deserción estudiantil en la educación superior.. Calidad enla Educación, (17), 91-108. Himmel, E. (2002). doi:https://doi.org/10.31619/caledu.n17.409spa
dc.relation.referencesBehavior Approach Model of Voluntary University Desertion in Undergraduate Programs in Engineering, Period 2015 2018. Ingeniería Solidaria, 14(26), 1–27 Sánchez Arévalo, M. L., Cruz Hueso, L. F., & Ferro Escobar, R. (2018) https://doi.org/10.16925/in.v14i26.2433spa
dc.relation.referencesBarone,S.,Chakhunashvili,A.,Comelli,A. (2020). Buldingastadisticalsurveillance dashboard forcovid-19infectionwolrwide.QualityEnginnering,(),.spa
dc.relation.referencesCEPAL,OPS (2021). La prolongación de la crisis sanitaria y su impacto en la salud, la economía yeldesarrollosocial,10-13spa
dc.relation.referencesColombia confirmasuprimercasodecovid-19.(2020).MinisteriodeSalud.Retrieved from https://www.minsalud.gov.co/Paginas/Colombia-confirma-su-primer-caso-de-COVIDaspx#:~:text=Bogot%C3%A1%2C%206%20de%20marzo%20de,una%20paciente%20de% 2019%20a%C3%B1os.spa
dc.relation.referencesPerla,R.,Provost,S.M.,Parry,G.,Little,K.,Provost,L. (2020). Understandingva- riation inreportedcovid-19deathswithanovelshewhartchartapplication.International Journal forQualityinHealthCare,(),1-8.spa
dc.relation.referencesZhao, S.,Q.Lin,J.,etal. (2020). Preliminaryestimationofthebasicreproductionnum- ber ofnovelcoronavirus(2019.ncov)inchina,from2019to2020.InternationalJournal of InfectiousDisaese,92(),214–7 UNALspa
dc.relation.referencesANSELIN, L., Spatial Econometrics:MethodsandModels, SpringerScienceyBusiness Media, Dordrecht,1988spa
dc.relation.referencesAZNAR,A.,J.MUR Y F.J.TRÍVEZ, Métodos econométricosenelanálisisregional Actas delaXXIIReunióndeEstudiosRegionales,delaAsociaciónEspañoladeCiencia Regional.Pamplona/Iruña,20-22denoviembrede1996;pp.237-264,1996.spa
dc.relation.referencesCHASCO, Y.C., Análisis estadísticodedatosgeográficosengeomarketing:elprograma GeoDa, 2006.spa
dc.relation.referencesDOMÍNGUEZ, A., Homicide ratesandhousingpricesinCaliandBogotáD.C,Cuadernos de Economía, 40(83),643-677.2021.spa
dc.relation.referencesGUILLEN GALICIA, M.Á., Factordedeméritoenlasviviendasencoloniasconproblemas de delincuencia(Master’sthesis), 2016.spa
dc.relation.referencesMORENO SERRANO, ROSINA, VAYÁ, ESTHER VALCARCE , Econometría espacial:nue- vas técnicasparaelanálisisregional.Unaaplicaciónalasregioneseuropeas, 2002.spa
dc.relation.referencesYRIGOYEN, C.C., Econometría espacialaplicadaalapredicción-extrapolacióndedatos microterritoriales.DirecciónGeneraldeEconomíayPlanificación, 2003.spa
dc.relation.referencesGarcía-Holgado, A., Camacho Díaz, A., García-Peñalvo, F. J. (2019). Engaging women into STEM in Latin America: W-STEM project In M. Á. Conde-González, F. J. Rodríguez- Sedano, C. Fernández-Llamas, F. J. García-Peñalvo (Eds.), TEEM’19 Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multicultu- rality (Leon, Spain, October 16th-18th, 2019) (pp. 232-239). New York, NY, USA: ACM. doi10.1145/3362789.3362902spa
dc.relation.referencesVerdugo-Castro, S. (2019, 2 julio). Revisión y estudio cualitativo sobre la brecha de género en el ámbito educativo STEM por la influencia de los estereotipos de género | CIAIQ2019. proceedings. https://proceedings.ciaiq.org/index.php/CIAIQ2019/article/view/2262spa
dc.relation.referencesTrapero, A. F. G. (2019). STEM y brecha de género en Latinoamérica. scielo. http://www.scielo.org.mx/scielo.php?script=sci_arttextpid=S1665-899X2019000100137 105spa
dc.relation.referencesArredondo T. Florina, Vázquez P. José, STEM y brecha de género en Latinoamérica, El Colegio de San Luis, SCIELO, San Luis Potosi, México,2019.spa
dc.relation.referencesForero Graciela, Herrera B. María Camila, Brechas de género en programas de ingeniería, Encuentro Internacional de Educación en Ingeniería, Cartagena de Indias, Colombia. 2021.spa
dc.relation.referencesPaladino Martin, Modelos logit con R, Instituto de Investigaciones Dr. José María Luis Mora, México, 2017.spa
dc.relation.referencesMULTINOMIAL LOGISTIC REGRESSION | R DATA ANALYSIS EXAMPLES, UCLA: Sta- tistical Consulting Group. tomado de: https://stats.oarc.ucla.edu/r/dae/multinomial-logistic- regression/ (tomado el 18-agosto-2022)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.referencesÁlvarez Amézquita, D. F., Salazar, Ó. E., & Padilla Herrera, J. C. (2015). Teoría de la propiedad intelectual. Fundamentos en la filosofía, el derecho y la economía 1. Civilizar 15 (28): 61-76, Enero-Junio de 2015. Retrieved 21 July 2022 from http://www.scielo.org.co/pdf/ccso/v15n28/v15n28a06.pdfspa
dc.relation.referencesArbeláez, M. U. (2007). LOS “BENEFICIOS” DEL TLC: LAS CONSECUENCIAS PARA LA BIODIVERSIDAD Y EL SISTEMA DE PROPIEDAD INTELECTUAL DE COLOMBIA. Pensamiento Jurídico, 0(18). Retrieved 21 July 2022 from https://revistas.unal.edu.co/index.php/peju/article/view/38604spa
dc.relation.referencesAuditoría general de la república. (2021). Reglamento de propiedad intelectual. Retrieved 21 July 2022 from Bogota : https://observatorio.auditoria.gov.co/documents/37869/1473507/Anexo+3.+Reglamento+de+propiedad+intelectual+RPI.pdf/ae2e2469-c378-b274-f4c3-15d10c0bc048?t=1652720671227spa
dc.relation.referencesBanco Mundial. (2022). Cargos por el uso de propiedad intelectual, pagos (balanza de pagos, US$ a precios actuales) - Colombia | Data. Retrieved 5 August 2022, from https://datos.bancomundial.org/indicador/BM.GSR.ROYL.CD?end=2021&locations=CO&name_desc=false&start=1968&view=chartspa
dc.relation.referencesDiaz Salvatierra, J. A. (2019). Efectos de los derechos de propiedad intelectual en la competitividad de las empresas ecuatorianas luego del Acuerdo Multipartes entre Ecuador y la Unión Europea. | Semantic Scholar. Retrieved 21 July 2022, from https://www.semanticscholar.org/paper/Efectos-de-los-derechos-de-propiedad-intelectual-en-D%C3%ADaz-Salvatierra/5f85553dc288a7b63fc3b07ad13d31ddea963122spa
dc.relation.referencesDuran, R. (2016). Derecho mercantil internacional, de Gerardo José Ravassa Moreno. IUSTA, 1(22). Retrieved 21 July 2022 from https://doi.org/10.15332/S1900-0448.2005.0022.11spa
dc.relation.referencesGisell, H., León, P., & Ferney Bonilla, D. (2008). ANALISIS DE LA BALANZA DE SERVICIOS DE PROPIEDAD INTELECTUAL EN EL PERIODO (2008-2018) EN COLOMBIA. Retrieved 21 July 2022 from http://repositorio.uan.edu.co/bitstream/123456789/1773/3/2020DarwinBonillaAvenda%c3%b1o.pdfspa
dc.relation.referencesLombardi, V. (2021). COVID-19 y patentes: el debate pendiente | Biodiversidad en América Latina. Retrieved 21 July 2022, from https://www.biodiversidadla.org/Documentos/COVID-19-y-patentes-el-debate-pendientespa
dc.relation.referencesMartínez, M. (2008). Generación y protección del conocimiento: propiedad intelectual, innovación y desarrollo económico Com isión Económ ica para Am érica Latina y el Caribe (CEPAL) Sede Subregional de la CEPAL en México. Retrieved 21 July 2022 from https://repositorio.cepal.org/handle/11362/2873?locale-attribute=enspa
dc.relation.referencesOrganizacion Mundial de Propiedad Intelectual. (2021). ¿Qué es la propiedad intelectual? Retrieved 21 July 2022 from https://www.wipo.int/publications/es/details.jsp?id=4528spa
dc.relation.referencesRomero, L. Q., & Mendoza, M. Á. (2016). ECONOMETRÍA APLICADA UTILIZANDO R.spa
dc.relation.referencesSuperintendencia de Industria y Comercio. (2017). Reporte sobre la información en materia de Propiedad Intelectual en Colombia. Retrieved 21 July 2022 from https://www.sic.gov.co/sites/default/files/files/Proteccion_Competencia/Estudios_Economicos/Documentos_elaborados_Grupo_Estudios_Economicos/Reporte-informacion-en-materia-de-Propiedad-Intelectual-en-Colombia.pdfspa
dc.relation.referencesNilsReimersandIrynaGurevych.Sentence-bert:Sentenceembeddingsusingsiamesebert- networks.In Proceedingsofthe2019ConferenceonEmpiricalMethodsinNaturalLanguage Processing. AssociationforComputationalLinguistics,112019.spa
dc.relation.referencesMontenegroÁlvaroMontegroDaniel.Diplomadoavanzadoiayap.In Repositorio Diplomado Avanzado. AprendizajeProfundo,062022.spa
dc.relation.referencesA. BADDELEY ET AL., Analysing point patterns on networks — a review. Spatial Statistics, vol. 42, p. 100435, 2021, towards Spatial Data Science.spa
dc.relation.referencesS. BYERS AND A. E. RAFTERY, Nearest-neighbor clutter removal for estimating features in spatial point processes. Journal of the American Statistical Association, vol. 93, no. 442, pp. 577–584, 1998spa
dc.relation.referencesE. L. KAPLAN AND PAUL MEIER, Nonparametric Estimation from Incomplete Observa- tions, Journal of the American Statistical Association, Vol. 53, No. 282 (Jun., 1958), pp. 457-481.spa
dc.relation.referencesEFRON, B., TIBSHIRANI, R. J. (1993), An introduction to the bootstrap New York, N.Y: Chapman Hall.spa
dc.relation.referencesAZZALINI, A. A class of distributions which includes the normal ones. Scandinavian Jour- nal of Statistics, 1985, 12, 171–178.spa
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 , DOI: 10.1080/03610926.2012.717666spa
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.referencesRIZZO, M., Statistical Computing with R. , Chapman Hall/CRC., 2019.spa
dc.relation.referencesKORKMAZ S, GOKSULUK D, ZARARSIZ G, “MVN: An R Package for Assessing Multivariate Normality.” The R Journal, 6(2), 151–162. https://journal.r-project.org/archive/2014- 2/korkmaz-goksuluk-zararsiz.pdf, 2019.spa
dc.relation.referencesJ. SZEKELY AND M. L. RIZZO, Energy statistics: A class of statistics based on distances Journal of Statistical Planning and Inference, 2013spa
dc.relation.referencesMARDIA, K. V., Applications of some measures of multivariate skewness and kurtosis for testing normality and robustness studies.,1974.spa
dc.relation.referencesCALHOUN, P.,LEVINE, R.&FAN, J., Repeated measuresrandomforests(RMRF):Iden- tifying factorsassociatedwithnocturnalhypoglycemia, Biometrics77,343–351,2021.spa
dc.relation.referencesDAVIS, C., Statistical MethodsfortheAnalysisofRepeatedMeasurements. First Edition, Springer,NewYork,2002.spa
dc.relation.referencesR,D.C.T., R: ALanguageandEnvironmentforStatisticalComputing. R Foundationfor Statistical Computing,Vienna,2022spa
dc.relation.referencesSELA, R.&SIMONOFF, J., Re-em trees:adataminingapproachforlongitudinaland clustereddata, MachineLearning86,169–207,2012.spa
dc.relation.referencesSELA, R.,SIMONOFF, J.&JING, W., RegressionTreeswithRandomEffectsforLongitu- dinal (Panel)Data. R Package.RFoundationforStatisticalComputing,Vienna,2021.spa
dc.relation.referencesLAWLESS, J., Statistical Models and Methods for Lifetime data, 2nd ed., John Wiley & Sons, Inc., 2003.spa
dc.relation.referencesGUO, B. AND WANG, B. X., Control charts for monitoring the Weibull shape parameter based on type-II censored samples. Quality and Reliability Engineering, 30, 13-24, 2014.spa
dc.relation.referencesPASUAL, F. AND LI, S., Monitoring the Weibull shape parameter by control charts for the sample range of type II censored data. Quality and Reliability Engineering, 28, 233-246, 2012.spa
dc.relation.referencesTartakovsky,A.,Nikiforov,I.,&Basseville,M.(2014).Sequentialanalysis:Hypothesis testing andchangepointdetection.CRCPress.spa
dc.relation.referencesMeeker,W.Q.,Escobar,L.A.,&Pascual,F.G.(2022).Statisticalmethodsforreliability data. JohnWiley&Sons.spa
dc.relation.referencesRausand,M.,&Hoyland,A.(2003).Systemreliabilitytheory:models,statisticalmethods, and applications(Vol.396).JohnWiley&Sons.spa
dc.relation.referencesLindqvist,B.H.,Elvebakk,G.,&Heggland,K.(2003).Thetrend-renewalprocessfor statistical analysisofrepairablesystems.Technometrics,45(1),31-44.spa
dc.relation.referencesJokiel-Rokita,A.,&Magiera,R.(2012).Estimationofparametersfortrend-renewalpro- cesses. Statisticsandcomputing,22(2),625-637.spa
dc.relation.referencesYang,Q.,Hong,Y.,Chen,Y.,&Shi,J.(2012).Failureprofileanalysisofcomplexrepairable systems withmultiplefailuremodes.IEEETransactionsonReliability,61(1),180-191.spa
dc.relation.referencesPage,E.S.(1954).Continuousinspectionschemes.Biometrika,41(1/2),100-115.spa
dc.relation.referencesBrown,R.L.,Durbin,J.,&Evans,J.M.(1975).Techniquesfortestingtheconstancyof regressionrelationshipsovertime.JournaloftheRoyalStatisticalSociety:SeriesB(Metho- dological), 37(2),149-163.spa
dc.relation.referencesWald,A.(1973).Sequentialanalysis.CourierCorporation.spa
dc.relation.referencesRoberts,S.W.(1959).Controlcharttestsbasedongeometricmovingaverages.Techno- metrics, 42(1),97-101.spa
dc.relation.referencesPage,E.S.(1961).Cumulativesumcharts.Technometrics,3(1),1-9.spa
dc.relation.referencesWasserman,L.(2006).Allofnonparametricstatistics.SpringerScience&BusinessMe- dia.spa
dc.relation.referencesBatanero, C.(2001):Did´acticadelaEstad´ıstica.UniversidaddeGranada.spa
dc.relation.referencesBatanero, C.(2013):Lacomprensi´ondelaprobabilidadenlosni˜nos:¿qu´epo- demos aprenderdelainvestigaci´on?spa
dc.relation.referencesBenjamin, D.(2018).ERRORSINPROBABILISTICREASONINGANDJUDG- MENTspa
dc.relation.referencesCortada, N.(2008):Lossesgoscognitivosenlatomadedecisions.International Journal ofPsychologicalResearch.1(1),68-73spa
dc.relation.referencesCostello, F.Mathison,T.(2014).OnFallaciesandnormativereasoning:when people´s judgementsfollowprobabilitytheory.spa
dc.relation.referencesD´ıaz,C.(2003):heur´ısticasysesgosenelrazonamientoprobabil´ısticoysus implicaciones enlaense˜nanzadelaestad´ıstica.spa
dc.relation.referencesFisk, J.(2005):Ageandprobabilisticreasoning:biasesinconjuntive,disjuntive and Bayesianjudgementsinearlyandlateadulthood.JournalofBehavioral decision making.18(1),55-82spa
dc.relation.referencesLabrador, M.(2016).Relevanciadelosfactorescognitivosenlosjuegosdeazar. Tesisdoctoral.UniversidadComplutensedeMadridspa
dc.relation.referencesRushdi, R.(2018):Commonfallaciesofprobabilityinmedicalcontext:Asimple mathematical exposition.JournalofadvancesinMedicineandMedicalresearch. 26(1),1-21spa
dc.relation.referencesSalcedo, A.&Mosquera,J.(2008).Sesgodeladisponibilidadenestudiantes universitarios.RecuperadadeInternet.Investigaci´onyPostgrado,vol.23,n.2, pp.411-432.spa
dc.relation.referencesSerrano,L., Batanero,C.,Ort´ız,J.&Ca˜nizares,J.(1998):Heur´ısticasyses- gos enelrazonamientoprobabil´ısticodelosestudiantesdesecundaria.10(1), 7-25spa
dc.relation.referencesSerrad´o,A.,Carde˜noso,J.&Azc´arate,P.(2005):Losobst´aculosenelapren- dizajedelconocimientoprobabil´ıstico:suincidenciadesdeloslibrosdetextospa
dc.relation.referencesFANDOS, C. Y FLAVIÁN, C., Las respuestas del consumidor ante la calidad percibida: una propuesta para productos agroalimentarios de calidad. Spanish journal of rural deve- lopment, 2(1), 37–52, 2011.spa
dc.relation.referencesGONZALES, M., Estudio de viabilidad comercial para una marca de miel de abeja para la Asociación de Productores Apícolas Cruz Verde del distrito de Íllimo, Chiclayo 2015spa
dc.relation.referencesHAIR, J.F., LDS GABRIEL, M., SILVA, D. Y BRAGA, S., Development and validation of attitudes measurement scales: fundamental and practical aspects. RAUSP Management Journal, 54, 490–507, 2019spa
dc.relation.referencesNKPURUKWE, O.I. Y OPARA, B.C., Social media marketing and customer fulfillment of airline operators in Nigeria. International Journal of Advancement in Strategic Manage- ment and Marketing, 9(2), 107–116, 2022.spa
dc.relation.referencesSARIS, W. E. Y GALLHOFER, I. N., Design, evaluation and analysis of questionnaires for survey research, John Wiley & Sons, 2014spa
dc.relation.referencesSCHOUTEN, C.N., Factors influencing beekeepers income, productivity and welfare in de- veloping countries: a scoping review. Journal of Apicultural Research, 60(2), 204–219, 2020spa
dc.relation.referencesLÓPEZ-RÍOS, V. I., Diseños óptimos para discriminación y estimación en modelos no lineales, Ph. D. Thesis: Centro de investigación en Matemáticas, 2008.spa
dc.relation.referencesDAVIS, J. L., PAPICH, M. G., MORTON, A. J., GAYLE, J., BLIKSLAGER, A. T. & CAMP- BELL, N. B. , Pharmacokinetics of etodolac in the horse following oral and intravenous administration, J. vet. Pharmacol. Therap. Vol. 30, 43–48, 2007.spa
dc.relation.referencesSTROUD, J. R., MULLER, P. & ROSNER, G. L., Optimal sampling times in population Pharmacokinetic studies, Applied Statistics. Vol 50, 3, 345–359, 2001.spa
dc.relation.referencesGLEN, I., Pharmacokinetic analysis, Anaesthesia and Intensive Care Medicine. Vol. 6, 8, 280–282, 2005.spa
dc.relation.referencesR CORE TEAM, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2021. URL https://www.R-project.org/.spa
dc.relation.referencesFIGUEREDO, L. VILLA-MURILLO, A., COLMERAREZ, Y Y VARQUEZ, C. , Journal of Insect Science. A hybrid artificial intelligence model for Aeneolamia varia (Hemiptera: Cercopidae) populations in sugarcane crops, 21(2), 1-6. doi: 10.1093/jisesa/ieab017, 2001.spa
dc.relation.referencesFIGUEREDO, L. HERNANDEZ, L. Y LINARES, B. , Revista Caña de Azúcar. Determi- nación de umbrales de daño fisiológico causados por la candelilla de la caña de azúcar Aeneolamia varia en los valles del Turbio y Yaracuy. Venezuela. 21, 3-19. 2003.spa
dc.relation.referencesVILLA-MURILLO, A., CARRIÓN, GARCIA Y SOZZI, ANTONIO , Publicaciones en cien- cias y tecnología. Optimización del diseño de parámetros: Método Forest-Genetic univa- riante, 10(1), 11-24. 2016.spa
dc.relation.referencesVILLA-MURILLO, A., CARRIÓN, GARCIA Y SOZZI, ANTONIO , Inteligencia artificial. Forest-Genetic method to optimize parameter design of multiresponse experiment , 23(66), 9-25. doi: 10.4114/intartif.vol23iss66pp9-25. 2020.spa
dc.relation.referencesO’Hagan,A,andOakley,J.E.(2004)ProbabilityisPerfect,butWeCan’tElicitItPer- fectly. Reliability EngineeringandSystemSafety, Vol.85,pp.239-248spa
dc.relation.referencesKadane,J.B.andWolfson,L.J.(1998)ExperiencesinElicitation. The Statistician, Vol. 47, No.1.,pp.3-1spa
dc.relation.referencesKaggle.com. Predict Severity and Deaths of COVID - 19 Patients. 2022. [online] Availa- ble at: https://www.kaggle.com/code/imperiopts/predict-severity-and-deaths-of-covid-19- patients [Accessed 27 August 2022].spa
dc.relation.referencesMORALES, J. Cronología de la pandemia en Georgia: Esto ha pasado a un año de detectar los primeros casos de coronavirus, 2021. Retrieved 25 June 2022, from https://www.univision.com/local/atlanta-wuvg/esto-ha-pasado-a-un-ano-de-detectar- los-primeros-casos-de-coronavirus-georgiaspa
dc.relation.references3] R CORE TEAM. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2021. https://www.R-project.org/spa
dc.relation.referencesEscofier,B.&Pagès,J.(1992), Análisis factorialessimplesymúltiples:objetivos,métodose interpretación, UniversidaddelPaísVascoBilbao.spa
dc.relation.referencesLebart, L.,Morineau,A.&Piron,M.(1995), Statistique exploratoiremultidimensionnelle, Vol.3,DunodParis.spa
dc.relation.referencesPardo,C.E.,Bécue-Bertaut,M.&Ortiz,J.E.(2013),‘Correspondenceanalysisofcontin- gencytableswithsubpartitionsonrowsandcolumns’, RevistaColombianadeEstadística 36(1), 115–144.spa
dc.relation.referencesCHETLUR, V.V.,DHILLON, H.S.&DETTMANN, C.P.,‘ShortestPathDistancein Manhattan PoissonLineCoxProcess’,2020.spa
dc.relation.referencesCHESSEL, D.,THIOULOUSE, J.DUFOUR, A,MADE4:AnalysisofEnvironmentalData:Ex- ploratory andEuclideanmethodMultivariatedataanalysisandgraphicaldisplay , Lyon,France , http://cran.univ-lyon1.fr/src/contrib/Descriptions/ade4.html,2005spa
dc.relation.referencesMINISTERIODE CIENCIAS, TECNOLOGIAE INNOVACION, ActoresdelSistemaNa- cional deCIencia,TecnologiaeInnovacion, Bogotá:ResoluciónNo.1473de2016. Obtenido dehttps://minciencias.gov.co/sites/default/files/ckeditor f iles/politiciadeactores − snctei.pd f ,2016.spa
dc.relation.referencesOBSERVATORIO COLOMBIANODE CIENCIAY TECNOLOGÍA - OCYT, Infor- me deIndicadoresdeCienciayTecnologíaColombia2020.OCyT , Obtenidode https://portal.ocyt.org.co/informe-de-indicadores-2020/,2021spa
dc.relation.referencesORGANIZACIÓNPARALA COOPERACIÓNYEL DESARROLLO ECONÓMICOS -OCDE- ,Manual deFrascati2015:Guíaparalarecopilaciónypresentacióndeinforma- ción sobrelainvestigaciónyeldesarrolloexperimental. Publishing,Paris/FEYCT. , doi:https://doi.org/10.1787/9789264310681-es,2018spa
dc.relation.referencesPARDO C.E.,BÉCUE, M.ORTIZ, J.,Métodosenejesprincipalesparatablasdecontingencia con estructurasdeparticiónenfilasycolumnas. Master´s thesis,UniversidadNacionalde Colombia, sedeBogotá. , https://repositorio.unal.edu.co/bitstream/handle/unal/51676 /campoeliaspardo.2011.pdf?sequence=1isAllowed=y,2011spa
dc.relation.referencesR DEVELOPMENT CORE TEAM,R: Alanguageandenvironmentforstatisticalcomputing,R . FoundationforStatisticalComputing,Vienna,Austria ,ISBN 3-900051-07-0.*http://www.R- project.org,202spa
dc.relation.referencesDOUGLAS C.MONTGOMERY, Introductiontostatisticalqualitycontrol , 7thedtition- Wiley,JohnWiley&Sons,2009.spa
dc.relation.referencesYOURSTONE, STEVEN A. AND ZIMMER, WILLIAM J., Non-Normality andtheDesignof ControlChartsforAverages Decision Sciences,1992.spa
dc.relation.referencesDENIS BAYART, Chapter 72-WalterAndrewShewhart,Economiccontrolofqualityof manufacturedproduct,ElsevierScience,1931.spa
dc.relation.referencesEDWARD G.SCHILLINGAND PETER R.NELSON, The EffectofNonNormalityonthe ControlLimitsof ¯X Charts, Taylor&Francis,1976.spa
dc.relation.referencesTESTIK, MURAT CANERAND WEISS, CHRISTIAN H AND KOCA, YESIMAND TESTIK, ÖZLEM MÜGE, FrontiersinStatisticalQualityControl12, AssessmentofShewhart control chartlimitsinphaseiimplementationsundervariousshiftandcontaminationsce- narios,pages 21–43,Springer,2018.spa
dc.relation.referencesRCORE TEAM, R: Alanguageandenvironmentforstatisticalcomputing, 2021spa
dc.relation.referencesNAES, T., A User-FriendlyGuidetoMultivariateCalibrationandClassification,2002.spa
dc.relation.referencesPORRAS, J.(2016).Comparacióndepruebasdenormalidadmultivariada. Anales Científi- cos. Volumen(77),141-146.spa
dc.relation.referencesSCHÄFER, J.,STRIMMER, K.(2005).AshrinkageApproachtoLarge-ScaleCovariance Matrix EstimationandImplicationsforFunctionalGenomics. Statistical Applicationsin Genetics andMolecularBiology. Vol.4:Iss.1,Article32.spa
dc.relation.referencesSHAPIRO, S.S.;WILK, M.B.(1965).Ananalysisofvariancetestfornormality(complete samples). Biometrika. 52 (3–4):591–611.spa
dc.relation.referencesVILLASEÑOR, J.,GONZÁLEZ, E.(2009).AGeneralizationofShapiro–Wilk’sTestfor Multivariatenormality. Communications inStatistics—TheoryandMethods. Volumen(38), 1870–1883.spa
dc.relation.references[AGENCIA NACIONALDE SEGURIDAD VIAL, Informe sobreSeguridadVialparaelCon- gresodelaRepública, pp.1–40,2021spa
dc.relation.referencesJ.D.ALARCÓN, I.GICH SALADICH, L.VALLEJO CUELLAR, A.M.RÍOS GALLARDO, C.MONTALVO ARCE, AND X.BONFILL COSP,, Mortalidad poraccidentesdetráficoen Colombia. Estudiocomparativoconotrospaíses Rev.Esp.SaludPublica,vol.92,2018spa
dc.relation.references[J.M.RODRIGUEZ HERNANDEZ, C.TOVAR FREDY, A.H.CIDRONIO, AND C.R.JULIO CESAR, Motorcyclists’MortalityPatterninColombiafrom2000to2013:ALongitudinal Study, Arch.Med.,vol.09,no.04,pp.1–9,2017,doi:10.21767/19895216.1000228.spa
dc.relation.references[V.CANTILLO, P.GARCÉS, AND L.MÁRQUEZ,, Factorsinfluencingtheoccurrenceof trafficaccidentsinurbanroads:AcombinedGIS-EmpiricalBayesianapproach, Dyna, vol.83,no.195,pp.21–28,2015,doi:10.15446/dyna.v83n195.47229.spa
dc.relation.references[F.GÓMEZAND J.P.BOCAREJO,, Accident predictionmodelsforbusrapidtransitsys- tems: Generalizedlinearmodelscomparedwithaneuralnetwork, Transp.Res.Rec.,vol. 2512, pp.38–45,2015,doi:10.3141/2512-05.spa
dc.relation.references[Y.LAO, G.ZHANG, Y.WANG, AND J.MILTON,, Generalizednonlinearmodels for rear-endcrashriskanalysis, Accid.Anal.Prev.,vol.62,pp.9–16,2014,doi: 10.1016/j.aap.2013.09.004..spa
dc.relation.references[P.LU AND D.TOLLIVER, Accident predictionmodelforpublichighway-railgradecros- sings, Accid.Anal.Prev.,vol.90,pp.73–81,2016,doi:10.1016/j.aap.2016.02.012.spa
dc.relation.references[Y.ZOU, L.WU, AND D.LORD, Modeling over-dispersedcrashdatawithalongtail: Examining theaccuracyofthedispersionparameterinNegativeBinomialmodels, Anal. Methods Accid.Res.,vol.5–6,pp.1–16,2015,doi:10.1016/j.amar.2014.12.002.spa
dc.relation.references[J.M.VER HOEFAND P.L.BOVENG, Quasi-PoissonvsBinomialRegression:How Should WeModelOverdispersedCountData?, Ecology,vol.88,no.11,pp.2766–2772, 2007.spa
dc.relation.referencesM.INGDAL, R.JOHNSEN, AND D.A.HARRINGTON, The Akaikeinformationcriterion in weightedregressionofimmittancedata, Electrochim.Acta,vol.317,pp.648–653,2019, doi: 10.1016/j.electacta.2019.06.030.spa
dc.relation.referencesJ.ANTOCH, IEnvironmentforstatisticalcomputing, Sci.Rev.,vol.2,no.2,pp.113–122, 2008, doi:10.1016/j.cosrev.2008.05.002.spa
dc.relation.referencesA.SALINAS-RODRÍGUEZ, B.MANRIQUE-ESPINOZA, AND S.G.SOSA-RUBÍ, Análisis estadístico paradatosdeconteo:Aplicacionesparaelusodelosserviciosdesalud, Salud Publica Mex.,vol.51,no.5,pp.397–406,2009,doi:10.1590/s0036-36342009000500007.spa
dc.relation.referencesA.HÚZGAME, La gravedaddelaaccidentalidadvialenColombiasuperalascifrasofi- ciales, Rev.Fasecolda,no.166,pp.16–19,2018.spa
dc.relation.referencesBersab´e,R.(1995).Sesgoscognitivosenlosjuegosdeazarlailusi´ondecon- trol. [Tesisdoctoral,UniversidadComplutensedeMadrid]spa
dc.relation.referencesBenjamin,D.(2019).Errorsinprobabilisticreasoningandjudgmentbiases. HandbookofBehavioralEconomics:ApplicationsandFoundations1,Volume2, Pages69-186.Disponibleenhttps://www.sciencedirect.com/science/article/pii/S2352239918300228)spa
dc.relation.referencesCortada,N.(2008).Lossesgoscognitivosenlatomadedecisiones.Interna- tional JournalofPsychologicalResearch,Vol.1,No.1,p.68-73.spa
dc.relation.referencesD´ıaz,C.delaFuente,I.(2005).Razonamientosobreprobabilidadcondicio- nal eimplicacionesparalaense˜nanzadelaestad´ıstica.Epsilon,59,245-260.spa
dc.relation.referencesD´ıaz,C.,Contreras,J.,Batanero,C.Roa,R.(2012).Evaluaci´ondeSes- gos enelRazonamientosobreProbabilidadCondicionalenFuturosProfeso- res deEducaci´onSecundaria.BoletimdeEduca¸c˜aoMatem´atica,26(44),1207- 1225.[fechadeConsulta29deEnerode2022].ISSN:0103-636X.Disponibleen: https://www.redalyc.org/articulo.oa?id=291226280007spa
dc.relation.referencesFushdi,R.RushdiA.(2018).Commonfallaciesofprobabilityinmedicalcon- text: asimplemathematicalexposition.Recuperadodeinternethttps://www.researchgate.net/publication/324502214CommonFspa
dc.relation.referencesHollyday,A.(2017).Top50listofcognitivebiasesintrading.Disponibleen https://capital.com/top-50-cognitive-biases-listspa
dc.relation.referencesJohnson-Laird,P.Khemlani,S.(2014).Towardaunifiedtheoryofreasoning. Disponibleenhttps://www.researchgate.net/publication/286039024T owardaunifiedtheoryofreasoning Mu˜noz,A.(2010).F alaciadeljugador.[Grabaci´on,CanalUNED].https : //descargas.uned.es/publico/pdf/AvisoLegalUNEDspa
dc.relation.referencesOjeda,A.M.(1995).Dificultadesdelalumnadorespectoalaprobabilidad condicional. UNO,5,37-55.spa
dc.relation.referencesOlgun,B.Isiksal-Bostan,M.(2019).Theinfluenceofthecontextofcondi- tional probabilityproblemsonprobabilisticthinking:Acasestudywithteacher candidates. EleventhCongressoftheEuropeanSocietyforResearchinMathe- matics Education(CERME11).Disponibleenhttps://hal.archives-ouvertes.fr/hal- 02412821/documentspa
dc.relation.referencesSimon,H.(1972).Elcomportamientoadministrativo.Madrid:Aguilar.spa
dc.relation.referencesTversky,A.Kahneman,(1976).D.Probabilisticreasoning.Disponibleen http://csinvesting.org/wp-content/uploads/2012/07/amostverskyanddanielkahneman−probabilisticreasoning2spa
dc.relation.referenceshttps://www.kaggle.com/datasets/caesarmario/our-world-in-data-covid19-dataset/ version/11 CAESAR, M.(2021).OurWorldinData-COVID-19.spa
dc.relation.referenceshttps://ourworldindata.org/coronavirus Ritchie,H. (2020,5marzo).Coronavirus Pandemic(COVID-19).OurWorldinData.spa
dc.relation.referencesTORRES IRRIBARRA, D. , A pragmatic perspective on measurement, New York: Springer Briefs in Psychology, 2020. https://doi.org/10.1007/978-3-030-74025-24)spa
dc.relation.referencesBall,Patrick,andMichaelH.Reed.2016.“ElRegistroyLaMedicióndeLaCriminali- dad. ElProblemadeLosDatosFaltantesyElUsodeLaCienciaParaProducirEstima- ciones EnRelaciónConElHomicidioEnColombia,DemostradoaPartirdeUnEjemplo: El DepartamentodeAntioquia(2003-2011)*.”RevistaCriminalidad58(1):9–23.spa
dc.relation.referencesBall,Patrick,andValentinaRozo.2018.“HomicidiosdeLíderesSocialesEnColombia En 2016–2017:UnaEstimaciónDelUniverso.”HumanRightsDataAnalysisGroup, Dejusticia Derecho-justicia-sociedad.spa
dc.relation.referencesBarbona,I.2014.AjustedeModelos“Car”ParaLaEstimacionEspacio-Temporalde Eventosspa
dc.relation.referencesCliff,A.D.,Ord,J.K.1981Spatialprocesses,Pion,p.21;BivandRS,WongDWS2018 Comparing implementationsofglobalandlocalindicatorsofspatialassociation.TEST, 27(3), 716–748doi:10.1007/s11749-018-0599-x.spa
dc.relation.referencesComisiónInteramericanadeDerechosHumanos.2019.InformeSobreLaSituaciónde Personas DefensorasdeDerechosHumanosyLíderesSocialesEnColombia.spa
dc.relation.referencesConsejoLatinoamericanodeCienciasSociales.2018.¿CuálesSonLosPatrones?Homi- cidios deLíderesSocialesEnElPostAcuerdospa
dc.relation.referencesDianaSantacruzGarcía.2020.“LíderesSocialesEnColombia:UnaMiradaDesdeLa Declaración UniversalSobreBioéticayDerechosHumanos.”JournalofChemicalInfor- mation andModeling21(1):1–9.spa
dc.relation.referencesErazo,LauraMaríaOrjuela.2019.8UniversidadJorgeTadeoLozano“LaTeoríaDel Modelo EnEspiralAplicadaAlHomicidiodeLíderesSocialesyDefensoresdeDerechos Humanos EnColombia:EntreLaResignaciónyElCambiospa
dc.relation.referencesINDEPAZ,CumbreAgrariacampesinaétnicaypopular,MarchaPatriótica.2020.Insti- tuto deEstudiosparaelDesarrolloylaPaz-INDEPAZInformeEspecial.Registrode Líderes yPersonasDefensorasdeDDHHAsesinadasDesdeLaFirmaDelAcuerdode Paz.Del24/11/2016Al15/07/2020.spa
dc.relation.referencesIndepaz.2021.CifrasdeLaViolenciaEnLasRegionespa
dc.relation.referencesGetis,ArthuryJ.K.Ord."TheAnalysisofSpatialAssociationbyUseofDistanceSta- tistics". GeographicalAnalysis24,Nº3.1992.spa
dc.relation.referencesGoodchild,MichaelF.SpatialAutocorrelation.Catmog47,GeoBooks.1986.spa
dc.relation.referencesGriffith,Daniel.SpatialAutocorrelation:APrimer.ResourcePublicationsinGeography, Association ofAmericanGeographers.1987.spa
dc.relation.referencesMitchell,Andy.LaGuíadeEsriparaelanálisisSIG,Volumen2.EsriPress,2005.spa
dc.relation.referencesPedraza,Camilo.2016.“HomicidiosdeLíderesSocialesEn2015.”ElTiempo.http://www.eltiempo.com/de-paz/homicidios-de-lideres-sociales-en-2015/16546681?ts=98.spa
dc.relation.referencesREQUENA, Matemáticas paraelturismoypordoquier, LasPalmas:Federaciónespañola de sociedadesdeprofesoresdematemáticas,2017.spa
dc.relation.referencesCharnes,A.,Cooper,W.W.,andRhodes,E.(1978).Measuringtheefficiencyofdecision- making units.EurJOperRes2,429–444spa
dc.relation.referencesBanker,R.D.,Charnes,A.,andCooper,W.(1984).Somemodelsforestimatingtechnical and scaleinefficienciesindataenvelopmentanalysis.ManagementScience,30(9),1078- 1092.spa
dc.relation.referencesBogetoft,P.,andHougaard,J.L.(1999).EfficiencyEvaluationsBasedonPoten- tial (Non-proportional)Improvements.JournalofProductivityAnalysis,12(3),233–47. doi:10.1023/A:1007848222681.spa
dc.relation.referencesMancebon,M.J.,andMuniz,M.A.(2007).PrivateversuspublichighschoolsinSpain: disentangling managerialandprogrammeefficiencies,JournaloftheOperationalResearch Society,1–10.spa
dc.relation.referencesDarji,I.S.,andDahiya,S.(2021).FinancialperformanceofthetextileindustryinIn- dia: thecaseofHaryana.ResearchJournalofTextileandApparel,ahead-of-print.doi: 10.1108/RJTA-06-2021-0083spa
dc.relation.referencesCunha,M.,andRocha,V.(2012).OntheEfficiencyofPublicHigherEducationInstitutions in Portugal:AnExploratoryStudy,FEPworkingpapers,No,468spa
dc.relation.referencesAhn,T.,Charnes,A.,andCooper,W.(1998).SomestatisticalandDEAevaluationsof relativeefficienciesofpublicandprivateinstitutionsofhighereducation,Socio-Economic Planning Sci,22,259–269spa
dc.relation.referencesBradley,S.,JohnesG.,andMillington,J.(2001).Theeffectofcompetitionontheefficiency of secondaryschoolsinEngland,EuropeanJournalOperationalResearch,135,545–568.spa
dc.relation.referencesMizala,A.,Romaguera,P.,andFarren,D.(2002).Thetechnicalefficiencyofschoolsin Chile, AppliedEconomics,34,1533–1552.spa
dc.relation.referencesBarbetta,G.P.,andTurati,G.(2003).Efficiencyofjuniorhighschoolsandtheroleof proprietary structure,AnnalsofPublicandCooperativeEconomics74:529–551.spa
dc.relation.referencesAsmild,M.,Holvad,T.,Hougaard,J.,andKronborg,D.(2009).Railwayreforms:dothey influence operatingefficiency?Transportation,36(5),617-638.spa
dc.relation.referencesGongbing,B.,Pingchun,W.,Feng,Y.,andLiang,L.(2014).EnergyandEnvironmental EfficiencyofChina’sTransportationSector:AMultidirectionalAnalysisApproach.Mat- hematical ProblemsinEngineering,1-12.ArticleID539596.doi:10.1155/2014/539596.spa
dc.relation.referencesWang,K.,Yu,S.,Li,M.J.,andWei,Y.(2015).Multi-directionalefficiencyanalysis-based regionalindustrialenvironmentalperformanceevaluationofChina.NaturalHazards.2015.spa
dc.relation.referencesHougaardJL,KronborgD,Overgard,C.ImprovementPotentialinDanishElderlyCare. Health CareManagementScience,2004;7(3):225–235.spa
dc.relation.referencesAsmildM,BalezentisT,HougaardJL.Multi-directionalProductivityChange:MEAMal- mquist, JournalofProductivityAnalysis,2016;46:109–119.spa
dc.relation.referencesMurilloK.RochaE.Sustainabledevelopmentineducation:anon-parametricanalysis. INTED2021 Proceedings,4536-4544,2021.doi:10.21125/inted.2021.0922spa
dc.relation.referencesMurillo,K.P.,Rocha,E.M.,andRamalho,J.S.(2018).AbouttheEfficiencyBehavior of thePortugueseManufacturingFirmsDuringtheFinancialCrisis.LibertasMathematica (newseries),38(1),1–27.spa
dc.relation.referencesPearsonK.OnLinesandPlanesofClosestFittoSystemsofPointsinSpace.TheLon- don, Edinburgh,andDublinPhilosophicalMagazineandJournalofScience,1901;2(11): 559–72. doi:10.1080/14786440109462720spa
dc.relation.referencesAbdi,H.,andWilliams,L.J.(2010).PrincipalComponentAnalysis.WileyInterdiscipli- nary Reviews:ComputationalStatistics,2(4),433–59.doi:10.1002/wics.101.spa
dc.relation.referencesDray,Stéphane,2008.Onthenumberofprincipalcomponents:atestofdimensionality based onmeasurementsofsimilaritybetweenmatrices.ComputationalStatisticsandData Analysis 52(4),2228–2237.doi:10.1016/j.csda.2007.07.015.spa
dc.relation.referencesJahanshahloo,G.R.,Lotfi,F.Hosseinzadeh,Shoja,N.,Tohidi,G.,Razavyan,S.,2005. Undesirable inputsandoutputsinDEAmodels.Appl.Math.Comput.169(2),917–925. doi: 10.1016/j.amc.2004.09.069spa
dc.relation.referencesRaith,A.,Fauzi,F.,andPerederieieva.,O.(2016).PyDEAdocumentation. https://araith.github.io/pyDEAspa
dc.relation.references[Castro et al., 2018]Castro,Wilson,Oblitas,Jimy,Maicelo,Jorge,&Avila-George,Himer. 2018. EvaluationofExpertSystemsTechniquesforClassifyingDifferentStagesofCoffee Rust InfectioninHyperspectralImages. International JournalofComputationalIntelligence Systems, 11(01), 86–100.spa
dc.relation.references[Ignacio Cascos&Romo,2011]IgnacioCascos,ÁngelLópez,&Romo,Juan.2011.Data depth inMultivariateStatistics. Boletín deEstadísticaeInvestigaciónOperativa, 27, 151– 174.spa
dc.relation.references[Rousseeuw &Hubert,2018]Rousseeuw,PeterJ.,&Hubert,Mia.2018.Anomalydetection by robuststatistics. WIREs DataMiningandKnowledgeDiscovery, 8(2), e1236.spa
dc.relation.references[Velásquez et al., 2020]Velásquez,David,Sánchez,Alejandro,Sarmiento,Sebastian,Toro, Mauricio, Maiza,Mikel,&Sierra,Basilio.2020.AMethodforDetectingCoffeeLeafRust through WirelessSensorNetworks,RemoteSensing,andDeepLearning:CaseStudyofthe Caturra VarietyinColombia. Applied Sciences, 10(2).spa
dc.relation.referencesC. Berejnoi, Comparación de diferentes modelos de la Teoría de Respuesta al Ítem MInves- tigaciones en Facultades de Ingeniería del NOA ISSN N° 1853-7871, Universidad Nacional de Salta, 2018.spa
dc.relation.referencesE. San Martín & P. De Boeck. Quantitative Psychology Research. Chapter: What do you mean by a difficult item? On the interpretation of the difficulty parameter in a Rasch model. Springer Proceedings in Mathematics & Statistics, 2015spa
dc.relation.referencesP. Fariña, J. González & E. San Martín. The use of an identifiability-based strategy for the interpretation of parameters in the 1PL-G and Rasch models. Psychometrika, 84(2):511– 528, 2019spa
dc.relation.referencesJ. Brezezinska Politomous item response theory models using R Econometrika, 2(52):44- 62, 2016.spa
dc.relation.referencesF. Tuerlinckx & P. De Boeck. Two interpretations of the discrimination parameter. Psycho- metrika, 70(4):629–650, 2005.spa
dc.relation.referencesP. Mair, Modern Psychometrics with R, Springer, 2018spa
dc.relation.referencesFitzmaurice, G.,Laird,N.,andWare,J.(2011).AppliedLongitudinalAnalysis.Jhon WileyandSonsspa
dc.relation.referencesHur,K.,etal.,(2002).ModelingClusteredCountDatawithExcessZerosinHealth Care OutcomesResearch.HealthServices&OutcomesResearchMethodology,Vol. 3. pp.5-20.spa
dc.relation.referencesHartig, J.(2015).DHARMa:residualdiagnosticsforhierarchical(multi-level/mixed) regressionmodels.UniversityofRegensburgspa
dc.relation.referencesNieuwenhuis, R.,Grotenhuis,MandPelzer,B.(2012).influence.ME:ToolsforDe- tecting InfluentialDatainMixedEffectsModels.TheRJournal.Vol.4,No.2.pp. 38-47.spa
dc.relation.referencesOrtiz,A.(11de01de2019).PorquéestanimportanteelanálisisBigData.Obtenido de Hostdimeblog:https://blog.hostdime.com.co/por-que-es-tan-importante-el-analisis-big- dataspa
dc.relation.referencesMissier,P.,Romanovsky,A.,Miu,T.,Pal,A.,Da,M.,&daSilvaSousa,L.(05de 10 de2016).TrackingDengueEpidemicsUsingTwitterContentClassificationand TopicModelling.InternationalConferenceonWebEngineering,80-92.Obtenidode https://bibliotecavirtual.uis.edu.co:2142/chapter/10.1007/978-3-319-46963-8_7spa
dc.relation.referencesStieglitz,S.,&Bruns,A.(03de2013).Insightsfromhashtag#supplychain and TwitterAnalytics:ConsideringTwitterandTwitterdataforsupplychain practice andresearch.InternationalJournalofSocialResearchMethodology, 16(2), 91-108.Obtenidodehttps://www.scopus.com/record/display.uri?eid=2-s2.0- 84874484025&origin=inward&txGid=b960067d0b9554be42571fbf32a0431fspa
dc.relation.referencesTwitter.(15de05de2018).Centrodeayuda.ObtenidodeIn- formación sobrelasAPIdeTwitter:https://help.twitter.com/es/rules- and-policies/twitter-api#::text=Twitter%20permite%20acceder%20 a %20partes,opini%C3%B3n%20del%20cliente%20en%20Twitter.spa
dc.relation.referencesKalyanam,J.,Katsuki,T.,Lanckriet,G.,&Mackey,T.(02de2017).Exploring trends ofnonmedicaluseofprescriptiondrugsandpolydrugabuseintheTwittersphe- re usingunsupervisedmachinelearning.AddictiveBehaviors,289-295.Obtenidode https://bibliotecavirtual.uis.edu.co:2191/science/article/pii/S0306460316302994#bb0150spa
dc.relation.referencesSorin,M.(2011).AnalyzingSocialMediaNetworkswithNodeXL:Insightsfrom a ConnectedWorldbyDerekHansen,BenShneiderman,andMarcA.Smith. International JournalofHuman–ComputerInteraction,405-408.Obtenidode https://www.tandfonline.com/doi/abs/10.1080/10447318.2011.544971?journalCode=hihc20spa
dc.relation.referencesMarqués,P.,Gonzales,M.,Vega,J.,Pinto,A.,&Quiroga,E.(2013).El analisis delasredessociales.Unmétodoparalamejoradelaseguridad en lasorganizacionessanitarias.RevEspSaludPública,209-219.Obtenidode http://scielo.isciii.es/pdf/resp/v87n3/01_colaboracion_especial.pdfspa
dc.relation.referencesKagashe,I.,Zhijun,Y.,&Mphil,I.(12de09de2017).EnhancingSeasonalInfluenza Surveillance:TopicAnalysisofWidelyUsedMedicinalDrugsUsingTwitterData.Journal Of MedicalInternetResearch,19(9).Obtenidodehttps://www.jmir.org/2017/9/e315/spa
dc.relation.referencesChoi,S.,Lee,J.,Kang,M.,Min,H.,Yoon,C.,Yoon,&Sungroh.(01de10de 2017). Large-scalemachinelearningofmediaoutletsforunderstandingpublicreac- tions tonation-wideviralinfectionoutbreaks.Methods,129(1),50-59.Obtenidode https://bibliotecavirtual.uis.edu.co:2191/science/article/pii/S1046202317300191#!spa
dc.relation.referencesField,A.,Miles,J.,&Field,Z.(2012).Discoveringsta- tistics usingR.(S.P.Ltd.,Ed.)1°edición.Obtenidode https://aedmoodle.ufpa.br/pluginfile.php/401852/mod_resource/content/5/Material_PDF/ 1.Discovering%20Statistics%20Using%20R.pdfspa
dc.relation.referencesX. He,Z.-Y.Zhu,andW.-K.Fung.Estimationinasemiparametricmodelforlongitudinaldata with unspecifieddependencestructure. Biometrika, 89(3):579–590,2002.spa
dc.relation.referencesB. JonesandM.G.Kenward. Design andAnalysisofCross-OverTrialsThirdEdition. Chap- man &Hall/CRC,BocaRaton,2015.spa
dc.relation.referencesL. S.McDaniel,N.C.Henderson,andP.J.Rathouz.FastpureRimplementationofGEE:ap- plication oftheMatrixpackage. The RJournal, 5:181–187,2013.URL https://journal. r-project.org/archive/2013-1/mcdaniel-henderson-rathouz.pdf.spa
dc.relation.referencesR CoreTeam. R: ALanguageandEnvironmentforStatisticalComputing. RFoundationfor Statistical Computing,Vienna,Austria,2022.URL https://www.R-project.org/.spa
dc.relation.referencesSamuel L Brilleman et al. “Bayesian survival analysis using the rstanarm R package”. In: arXiv preprint arXiv:2002.09633 (2020).spa
dc.relation.referencesDavid R Cox. “Partial likelihood”. In: Biometrika 62.2 (1975), pp. 269–276.spa
dc.relation.referencesDavid R Cox. “Regression models and life-tables”. In: Journal of the Royal Statistical So- ciety: Series B (Methodological) 34.2 (1972), pp. 187–202.spa
dc.relation.referencesAndrew Gelman et al. Bayesian data analysis. Chap- man and Hall/CRC, 1995.spa
dc.relation.referencesBrandon George, Samantha Seals, and Inmaculada Aban. “Survival analysis and regression models”. In: Journal of nuclear cardiology 21.4 (2014), pp. 686– 694.spa
dc.relation.referencesDHS Program. Demographic and health surveys (DHS). 2021.spa
dc.relation.referencesAndrewGelman,JohnBCarlin,HalSStern,andDonaldBRubin. Bayesian dataanalysis. Chapman andHall/CRC,1995.spa
dc.relation.referencesChristianHundt,MoritzSchlarb,andBertilSchmidt.Sauce:Awebapplicationforinter- activeteachingandlearningofparallelprogramming. JournalofParallelandDistributed Computing, 105:163–173,2017.spa
dc.relation.referencesYen-TingLinandMinJou.Integratingpopularwebapplicationsinclassroomlearningenvi- ronments anditseffectsonteaching,studentlearningmotivationandperformance. Turkish Online JournalofEducationalTechnology-TOJET, 12(2):157–165,2013.spa
dc.relation.referencesGailPotter,JimmyWong,IrvinAlcaraz,PeterChi,etal.Webapplicationteachingtools for statisticsusingrandshiny. TechnologyInnovationsinStatisticsEducation, 9(1),2016.spa
dc.relation.referencesGE, M.F.,LIU, Y.,JIANG, X.,&LIU, J. A reviewonstateofhealthestimationsand remainingusefullifeprognosticsoflithium-ionbatteries. Measurement, 174,109057,2021.spa
dc.relation.referencesHU, X.,XU, L.,LIN, X.,&PECHT, M. Battery LifetimePrognostics. Joule, 4(2), 310–346, 2020.spa
dc.relation.referencesLIU, H.C.,CHENG, C.H.,CHUN-YEN, W.,KUNG-MING, J.,CHEN, Y.G.,CHUNG, C.,CHIH-CHIEN, S.,&YONG-KAI, L. Microcontrollerunitbasedmotorcontrolsystem using fieldorientedcontrolalgorithm. ICICS 2013-ConferenceGuideofthe9thInterna- tional ConferenceonInformation,CommunicationsandSignalProcessing,2013.spa
dc.relation.referencesNANAKI, E.A. Electric vehiclecapitals–casestudies. Electric VehiclesforSmartCities, 181–247, 2021.spa
dc.relation.referencesSAHA, B.,&GOEBEL, K. Battery DataSet. NASAAmesResearchCenter.MoffettField, CA, 2007.spa
dc.relation.referencesUPME. EstrategiaNacionaldeMovilidadEléctrica. Unidad dePlaneaciónMineroEner- gética, 2019.spa
dc.relation.referencesZHANG, X.,LI, Z.,LUO, L.,FAN, Y.,&DU, Z. A reviewonthermalmanagementof lithium-ion batteriesforelectricvehicles. Energy,238,121652,2022.spa
dc.relation.referencesAGRESTI, A., FoundationsofLinearandGeneralizedLinearModels Wiley,2015.spa
dc.relation.referencesKHAN, G.TARIQ, Z. Y MUHAMMAD, M., Multi-PersonTrackingBasedonFasterR-CNN and DeepAppearanceFeatures IntechOpen, 2019.spa
dc.relation.referencesSHAOQING, R.KAIMING, H.ROSS, G.YJIAN, S., FasterR-CNN:TowardsReal-Time Object DetectionwithRegionProposalNetworks. Institute ofElectricalandElectronics Engineers, 2017.spa
dc.relation.referencesVANEGAS, L. Y RONDÓN, M., Notas declase:ModelosLinealesGeneralizados Univer- sidad NacionaldeColombia,2018.spa
dc.relation.referencesWANG, Q.ZHANG L. Y BERTINETTO, L., FastOnlineObjectTrackingandSegmentation: A UnifyingApproach. IEEE ConferenceOnComputerVisionAndPatternRecognition, 2019.spa
dc.relation.referencesWARREN, S., NeuralNetworksandStatisticalModels. Proceedings oftheNineteenthAn- nual SASUsersGroupInternationalConference,1994. @articleRen2017,year=2017, publisher=InstituteofElectricalandElectronicsEngineers(IEEE), volume= 39,number=6, pages=1137−−1149,author=ShaoqingRenandKaimingHeandRossGirshickandJianSun, FasterR−CNN : TowardsReal−TimeObjectDetectionwithRegionProposalNetworks, journal = IEEETransactionsonPatternAnalysisandMachineIntelligencespa
dc.relation.referencesDiazValencia,P.A.DoctoradoenEpidemiología,UniversidaddeAntioquia,2022.spa
dc.relation.referencesGelman,Andrew.Analysisofvariance:Whyitismoreimportantthanever. The AnnalsofStatistics,33(1),1-53.spa
dc.relation.referencesSecretaríadesaluddeMedellín,UnidaddeGestióndelaInformaciónyelConoci- miento. Desnutrición agudaenmenoresde5años. MEData. http://medata.gov. co/dataset/desnutrici%C3%B3n-aguda-en-menores-de-5-a%C3%B1os, 2019.spa
dc.relation.referencesS.MAKRIDAKIS, E.SPILIOTIS & V.ASSIMAKOPOULOS (2020), The M4Competi- tion: 100,000timeseriesand61forecastingmethods, InternationalJournalofForecasting, 36(1):54–74, DOI:10.1016/j.ijforecast.2019.04.01410.1080/23249935.2021.1990438.spa
dc.relation.referencesS.HOCHREITER & J.SCHMIDHUBER (1997), Long ShortTermMemory, NeuralCompu- tation, 9(8):1735–1780,DOI:10.1162/neco.1997.9.8.1735.spa
dc.relation.referencesS.MAKRIDAKISAND E.SPILIOTISAND V.ASSIMAKOPOULOS (2018), Statistical and MachineLearningforecastingmethods:Concernsandwaysforward, PLOSONE,13(3), 10.1371/journal.pone.0194889.spa
dc.relation.referencesV.FLOVIK (2018), How (not)touseMachineLearningfortimeseriesforecasting:Avoi- ding thepitfalls, Accessed:2022-04-20,URL:https://towardsdatascience.com/how-not-to- use-machine-learning-/for-time-series-forecasting-avoiding-the-pitfalls-19f9d7adf424.spa
dc.relation.referencesSHEREEN ELSAYEDAND DANIELA THYSSENSAND AHMED RASHEDAND HADI SAMER JOMAAAND LARS SCHMIDT-THIEME (2021), Do WeReallyNeedDeepLearning Models forTimeSeriesForecasting?, DOI:10.48550/arXiv.2101.02118.spa
dc.relation.referencesV.CERQUEIRAAND L.TORGOAND C.SOARES (2019), MachineLearningvsStatistical Methods forTimeSeriesForecasting:SizeMatters, DOI:10.1371/journal.pone.0194889.spa
dc.relation.referencesGUIBERT, Q. AND LOISEL, S. AND LOPEZ, O. AND PIETTE, P., Bridging the Li-Carter’s gap: a locally coherent mortality forecast approach, 2020. hal-02472777spa
dc.relation.referencesCARRACEDO, P. AND DEBÓN, A. AND IFTIMI, A. AND MONTES, F., Detecting spatio- temporal mortality clusters of European countries by sex and age. International journal for equity in health, Vol. 17, BioMed Central, 2018.spa
dc.relation.referencesBARBIERI, M. AND DEPLEDGE, R., Mortality in France by département. Population, Vol. 68 (3), 375-417, 2013.spa
dc.relation.referencesAdam,L.,&Bejda,P.(2018).Robustestimatorsbasedongeneralizationoftrimmed mean. Communications inStatistics-SimulationandComputation, 47(7), 2139-2151.spa
dc.relation.referencesAkritas,M.G.,Arnold,S.F.,&Brunner,E.(1997).Nonparametrichypothesesandrank statistics forunbalancedfactorialdesigns. Journal oftheAmericanStatisticalAssocia- tion, 92(437), 258-265.spa
dc.relation.referencesRCoreTeam(2017).R:Alanguageandenvironmentforstatisticalcomputing.RFoun- dation forStatisticalComputing,Vienna,Austria. • https://rpubs.com/MezaStat/nonparametricspa
dc.relation.referencesT. Harko, F. S. N. Lobo, and M. K. Mak. Exact analytical solutions of the susceptible- infected-recovered (sir) epidemic model and of the sir model with equal death and birth rates. Applied Mathematics and Computation, 236, 2014.spa
dc.relation.referencesA. G. Hawkes. Spectra of some self-exciting and mutually exciting point processes. Bio- metrika, 58(1):83–90, 1971spa
dc.relation.referencesD. Higdon, J. L. Swall, and J. Kern. Non-stationary spatial modeling. Bayesian Statistics, 6, 1998.spa
dc.relation.referencesY. Ogata. Space-time point-process models for earthquake occurrences. Annals of the Institute of Statistical Mathematics, 50(2):379–402, 1998.spa
dc.relation.referencesPresidency of the Republic of Colombia. Decrees during the COVID-19 pandemic, 2020. https://coronaviruscolombia.gov.co/Covid19/decretos.html.spa
dc.relation.referencesA. Reinhart. A review of self-exciting spatio-temporal point processes and their applica- tions. Statistical Science, 2017.spa
dc.relation.referencesS. Zhu, A. Bukharin, L. Xie, K. Yamin, S. Yang, P. Keskinocak, and Y. Xie. Early detection of covid-19 hotspots using spatio-temporal data. IEEE Journal of Selected Topics in Signal Processing, pages 1–1, 2022.spa
dc.relation.referencesS. Zhu, R. Ding, M. Zhang, P. Van Hentenryck, and Y. Xie. Spatio-temporal point proces- ses with attention for traffic congestion event modeling. IEEE Transactions on Intelligent Transportation Systems, pages 1–12, 2021.spa
dc.relation.referencesS. Zhu, H. Wang, Z. Dong, X. Cheng, and Y. Xie. Neural spectral marked point processes. In International Conference on Learning Representations, 2022spa
dc.relation.referencesK. S. Chen y H. Tong. On estimating thresholds in autoregresive models. Journal of Time Series Analysis, 7(3):179–190, 1986.spa
dc.relation.referencesJ. González y F. H. Nieto. Bayesian analysis of multiplicative seasonal threshold autoregressive processes. Revista Colombiana de Estadística - Applied Statistics, 43(2):251–285, 2020.spa
dc.relation.referencesR. Tsay. Testing and modeling threshold autoregressive processes. Journal of the Ame- rican Statistical Association, 84((405)):231–240, 1989.spa
dc.relation.referencesP. A. Vaca. Analysis of the forecasting performance of the threshold autorregresive model. Tesis de maestría, Universidad Nacional de Colombia, 2018.spa
dc.relation.referencesAbdou, H.A.,&Pointon,J.(2011).Creditscoring,statisticaltechniquesandevaluationcriteria:areviewofthe literature. Intelligentsystemsinaccounting,financeandmanagement, 18(2-3), 59-88.spa
dc.relation.referencesBaesens, B.,VanGestel,T.,Viaene,S.,Stepanova,M.,Suykens,J.,&Vanthienen,J.(2003).Benchmarkingstate- of-the-art classificationalgorithmsforcreditscoring. Journaloftheoperationalresearchsociety, 54(6), 627-635.spa
dc.relation.referencesBreiman, L.(1996).Baggingpredictors. Machinelearning, 24(2), 123-140spa
dc.relation.referencesBreiman, L.,Friedman,J.,Stone,C.J.,&Olshen,R.A.(1984). Classification andregressiontrees. CRCpressspa
dc.relation.referencesCastaño, H.F.,&Ramírez,F.O.P.(2005).Elmodelologístico:unaherramientaestadísticaparaevaluarelriesgo de crédito. RevistaIngenieríasUniversidaddeMedellín, 4(6), 55-75.spa
dc.relation.referencesChen, C.,Liaw,A.,Breiman,L.,etal.(2004).Usingrandomforesttolearnimbalanceddata. UniversityofCali- fornia, Berkeley, 110(1-12), 24.spa
dc.relation.referencesCrook, J.,Thomas,L.,&Edelman,D.(2002). Creditscoringanditsapplications. SIAMspa
dc.relation.referencesDastile, X.,Celik,T.,&Potsane,M.(2020).Statisticalandmachinelearningmodelsincreditscoring:Asystematic literature survey. Applied SoftComputing, 91, 106263.spa
dc.relation.referencesGanganwar,V.(2012).Anoverviewofclassificationalgorithmsforimbalanceddatasets. International Journalof EmergingTechnologyandAdvancedEngineering, 2(4), 42-47.spa
dc.relation.referencesGenuer, R.,&Poggi,J.-M.(2020).Randomforests.En Random ForestswithR (pp. 33-55).Springer.spa
dc.relation.referencesHand, D.J.,&Henley,W.E.(1993).Canrejectinferenceeverwork? IMA JournalofManagementMathematics, 5(1), 45-55spa
dc.relation.referencesHand, D.J.,&Henley,W.E.(1997).Statisticalclassificationmethodsinconsumercreditscoring:areview. Journal of theRoyalStatisticalSociety:SeriesA(StatisticsinSociety), 160(3), 523-541.spa
dc.relation.referencesHenley,W.E.(1995). Statistical aspectsofcreditscoring. OpenUniversity(UnitedKingdom).spa
dc.relation.referencesLiaw,A.,Wiener,M.,etal.(2002).ClassificationandregressionbyrandomForest. R news, 2(3), 18-22spa
dc.relation.referencesMcCue, C.(2014). Data miningandpredictiveanalysis:Intelligencegatheringandcrimeanalysis. Butterworth- Heinemann.spa
dc.relation.referencesSchreiner, M.(2003).Scoring:thenextbreakthroughinmicrocredit. Occasional paper, 7.spa
dc.relation.referencesSullivan,W.(2017). MachineLearningForBeginnersGuideAlgorithms:Supervised&UnsupervsiedLearning. Decision Tree&RandomForestIntroduction. HealthyPragmaticSolutionsInc.spa
dc.relation.referencesVojtek,M.,Koèenda,E.,etal.(2006).Credit-scoringmethods. CzechJournalofEconomicsandFinance(Finance a uver), 56(3-4), 152-167.spa
dc.relation.referencesWang,G.,Hao,J.,Ma,J.,&Jiang,H.(2011).Acomparativeassessmentofensemblelearningforcreditscoring. Expert systemswithapplications, 38(1), 223-230spa
dc.relation.referencesWang,G.,Ma,J.,Huang,L.,&Xu,K.(2012).Twocreditscoringmodelsbasedondualstrategyensembletrees. Knowledge-BasedSystems, 26, 61-68.spa
dc.relation.referencesWiginton,J.C.(1980).Anoteonthecomparisonoflogitanddiscriminantmodelsofconsumercreditbehavior. JournalofFinancialandQuantitativeAnalysis, 15(3), 757-770.spa
dc.relation.referencesZhang, X.,Yang,Y.,&Zhou,Z.(2018).Anovelcreditscoringmodelbasedonoptimizedrandomforest. 2018 IEEE 8thAnnualComputingandCommunicationWorkshopandConference(CCWC), 60-65.spa
dc.relation.referencesNIETO-REYES, A., Random Projections:ApplicationstoStatisticalDataDepthand Goodness ofFitTest, Vol.35,No.1.,BoletíndeEstadísticaeInvestigaciónOperativa, 2019.spa
dc.relation.referencesCUESTA-ALBERTOETAL, A sharpformoftheCramér-Woldtheorem. Probab.,Springer, 20, 201–209.,2007.spa
dc.relation.referencesFRANKL, P., The Johnson-Lindenstrausslemmaandthesphericityofsomegraphs Combin. Theory Ser.B.,44(3),355–362.1988spa
dc.relation.referencesAnálisisdetexto(textmining)conPythonbyJoaquínAmatRodri- go, availableunderaAttribution4.0International(CCBY4.0)at https://www.cienciadedatos.net/documentos/py25-text-mining-python.htmlspa
dc.relation.referencesArcila-Calderón,C.,Ortega-Mohedano,F.,Jiménez-Amores,J.,Trullenque,S.(2017). Análisis supervisadodesentimientospolíticosenespañol:clasificaciónentiemporealde tweets basadaenaprendizajeautomático.ProfesionaldelaInformación,26(5),973-982.spa
dc.relation.referencesAruoba,S.B.,Fernández-Villaverde,J.(2015).Acomparisonofprogramminglanguages in macroeconomics.JournalofEconomicDynamicsandControl,58,265-273.spa
dc.relation.referencesCollomb,A.,Costea,C.,Joyeux,D.,Hasan,O.,Brunie,L.(2014).Astudyandcomparison of sentimentanalysismethodsforreputationevaluation.RapportderechercheRR-LIRIS- 2014-002.spa
dc.relation.referencesDashtipour,K.,Poria,S.,Hussain,A.,Cambria,E.,Hawalah,A.Y.,Gelbukh,A.,Zhou, Q. (2016).Multilingualsentimentanalysis:stateoftheartandindependentcomparisonof techniques. Cognitivecomputation,8(4),757-771.spa
dc.relation.referencesHernández,M.B.,Gómez,J.M.(2013).Aplicacionesdeprocesamientodelenguajenatu- ral. RevistaPolitécnica,32.spa
dc.relation.referencesHurtado,L.F.,Pla,F.,Buscaldi,D.(2015,September).ELiRF-UPVenTASS2015:Aná- lisis deSentimientosenTwitter.InTASS@SEPLN(pp.75-79).spa
dc.relation.referencesIslam,M.R.,Zibran,M.F.(2018,March).Acomparisonofsoftwareengineeringdomain specific sentimentanalysistools.In2018IEEE25thinternationalconferenceonsoftware analysis, evolutionandreengineering(SANER)(pp.487-491).IEEE.spa
dc.relation.referencesKirilenko,A.P.,Stepchenkova,S.O.,Kim,H.,Li,X.(2018).Automatedsentimentanaly- sis intourism:Comparisonofapproaches.JournalofTravelResearch,57(8),1012-1025spa
dc.relation.referencesKouatchou,J.,Medema,A.(2018).Basiccomparisonofpython,julia,matlab,idlandjava (2018 edition).ModelingGuru–NationalAeronauticsandSpaceAdministration,USA.spa
dc.relation.referencesLak,P.,Turetken,O.(2014,January).Starratingsversussentimentanalysis–acomparison of explicitandimplicitmeasuresofopinions.In201447thHawaiiinternationalconference on systemsciences(pp.796-805).IEEE.spa
dc.relation.referencesLópezBarbosa,R.R.(2015).Aplicacióndelanálisisdesentimientosalaevaluaciónde datos generadosenmediossociales.spa
dc.relation.referencesMisuraca,MichelangeloForciniti,AlessiaScepi,GermanaSpano,Maria.(2020).Senti- ment AnalysisforEducationwithR:packages,methodsandpracticalapplications.spa
dc.relation.referencesMorales,E.,Jorge,A.(2017).Comparaciónderendimientodetécnicasdeaprendizaje automático paraanálisisdeafectosobretextosenespañol.spa
dc.relation.referencesNaldi,M.(2019).AreviewofsentimentcomputationmethodswithRpackages.arXiv preprint arXiv:1901.08319.spa
dc.relation.referencesRackauckas,C.(2017).Acomparisonbetweendifferentialequationsolversuitesin MATLAB.R,Julia,Python,C,Mathematica,Maple,andFortran,TheWinnower, 5(e153459), 55spa
dc.relation.referencesCardot,H.,Crambes,C.,andSarda,P(2005). Quantile regressionwhenthecovariatesare functions, NonparametricStatistics,17(7),841-856.spa
dc.relation.referencesCraig,R.andKeming,Y.,(2009). Gibbs samplerforBayesianquantileregression, (JKe- ming Yu:DepartmentofMathematicalSciences,BrunelUniversity,Uxbridge)spa
dc.relation.referencesKoenker,R.(2005). Quantile regression, Vol38.Cambridgeuniversitypressspa
dc.relation.referencesH.Hashem,V.Vinciotti,R.Alhamzawi,K.Yu(sf). Quantile RegressionwithGroupLasso for Classication .(providedbyBrunelUniversityResearchArchivespa
dc.relation.referencesXu,X.andGhosh,M.(2015).Bayesian VariableSelectionandEstimationforGroupLasso. Bayesian Analysis.10,Number4,pp.909–936.spa
dc.relation.referencesArregocés, H. A., Rojano, R. & Restrepo, G. (2021), ‘Sensitivity analysis of planetary bound- ary layer schemes using the WRF model in Northern Colombia during 2016 dry season’, Dynamics of Atmospheres and Oceans 96(November 2020).spa
dc.relation.referencesBorge, R., Alexandrov, V., José del Vas, J., Lumbreras, J. & Rodríguez, E. (2008), ‘A compre- hensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula’, Atmospheric Environment 42(37), 8560–8574.spa
dc.relation.referencesBoylan, J. W. & Russell, A. G. (2006), ‘PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models’, Atmospheric Environment 40(26), 4946–4959spa
dc.relation.referencesCarvalho, D., Rocha, A., Gómez-Gesteira, M. & Santos, C. (2012), ‘A sensitivity study of the WRF model in wind simulation for an area of high wind energy’, Environmental Mod- elling and Software 33, 23–34. URL: http://dx.doi.org/10.1016/j.envsoft.2012.01.019spa
dc.relation.referencesCarvalho, D., Rocha, A., Gómez-Gesteira, M. & Silva Santos, C. (2014a), ‘Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula’, Applied Energy 135, 234–246.spa
dc.relation.referencesCarvalho, D., Rocha, A., Gómez-Gesteira, M. & Silva Santos, C. (2014b), ‘WRF wind simu- lation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal’, Applied Energy 117, 116–126. URL: http://dx.doi.org/10.1016/j.apenergy.2013.12.001spa
dc.relation.referencesDillon, M. E., Skabar, Y. G., Ruiz, J., Kalnay, E., Collini, E. A., Echevarría, P., Saucedo, M., Miyoshi, T. & Kunii, M. (2016), ‘Application of the WRF-LETKF Data Assimilation System over Southern South America: Sensitivity to Model Physics’, Weather and Fore- casting 31(1), 217–236. URL: http://journals.ametsoc.org/doi/10.1175/WAF-D-14-00157.1spa
dc.relation.referencesDu, J., Berner, J., Buizza, R., Charron, M., Houtekamer, P., Hou, D., Jankov, I., Mu, M., Wang, X., Wei, M. & Yuan, H. (2018), ‘Ensemble Methods for Meteorological Predictions’, Handbook of Hydrometeorological Ensemble Forecasting pp. 1–52.spa
dc.relation.referencesDzebre, D. E. & Adaramola, M. S. (2020), ‘A preliminary sensitivity study of Planetary Bound- ary Layer parameterisation schemes in the weather research and forecasting model to sur- face winds in coastal Ghana’, Renewable Energy 146, 66–86. URL: https://doi.org/10.1016/j.renene.2019.06.133spa
dc.relation.referencesEfron, B. & Tibshirani, R. J. (1993), An Introduction to the Bootstrap, Springer Series in statis- tics.spa
dc.relation.referencesEtherton, B. & Santos, P. (2006), ‘Sensitivity of WRF forecasts to initial and boundary condi- tions’, Bulletin of the American Meteorological Society 87(11), 1495–1496.spa
dc.relation.referencesEtherton, B. & Santos, P. (2008), ‘Sensitivity of WRF forecasts for South Florida to initial conditions’, Weather and Forecasting 23(4), 725–740.spa
dc.relation.referencesFalasca, S., Gandolfi, I., Argentini, S., Barnaba, F., Casasanta, G., Di Liberto, L., Petenko, I. & Curci, G. (2021), ‘Sensitivity of near-surface meteorology to PBL schemes in WRF simulations in a port-industrial area with complex terrain’, Atmospheric Research 264(May), 105824. URL: https://doi.org/10.1016/j.atmosres.2021.105824spa
dc.relation.referencesFarchi, A. & Bocquet, M. (2018), ‘Review article : Comparison of local particle filters and new implementations’, pp. 765–807.spa
dc.relation.referencesFernández-González, S., Martín, M. L., García-Ortega, E., Merino, A., Lorenzana, J., Sánchez, J. L., Valero, F. & Rodrigo, J. S. (2018), ‘Sensitivity analysis of the WRF model: Wind- resource assessment for complex terrain’, Journal of Applied Meteorology and Climatol- ogy 57(3), 733–753spa
dc.relation.referencesHenao, J. J., Mejía, J. F., Rendón, A. M. & Salazar, J. F. (2020), ‘Sub-kilometer dispersion sim- ulation of a CO tracer for an inter-Andean urban valley’, Atmospheric Pollution Research 11(February), 0–1. URL: https://doi.org/10.1016/j.apr.2020.02.005spa
dc.relation.referencesHill, A. J., Weiss, C. C. & Ancell, B. C. (2016), ‘Ensemble sensitivity analysis for mesoscale forecasts of dryline convection initiation’, Monthly Weather Review 144(11), 4161–4182.spa
dc.relation.referencesHu, X. M., Klein, P. M. & Xue, M. (2013), ‘Evaluation of the updated YSU planetary bound- ary layer scheme within WRF for wind resource and air quality assessments’, Journal of Geophysical Research Atmospheres 118(18), 490–10spa
dc.relation.referencesJankov, I., Gallus, W. A., Segal, M. & Koch, S. E. (2007), ‘Influence of initial conditions on the WRF-ARW Model QPF response to physical parameterization changes’, Weather and Forecasting 22(3), 501–519.spa
dc.relation.referencesJee, J. B. & Kim, S. (2017), ‘Sensitivity sudy on high-resolution WRF precipitation forecast for a heavy rainfall event’, Atmosphere 8(6).spa
dc.relation.referencesJones, L. A. & Woodall, W. H. (1998), ‘The performance of bootstrap control charts’, Journal of Quality Technology 30(4), 362–375.spa
dc.relation.referencesKumar, A., Jiménez, R., Belalcázar, L. C. & Rojas, N. Y. (2016), ‘Application of WRF-Chem Model to Simulate PM10 Concentration over Bogotá’, Aerosol and Air Quality Research 16(5), 1206–1221spa
dc.relation.referencesLangland, R. H. & Baker, N. L. (2004), ‘Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system’, Tellus A: Dynamic Meteorology and Oceanography 56(3), 189–201.spa
dc.relation.referencesLo, J. C.-F., Yang, Z.-L. & Pielke, R. A. (2008), ‘Assessment of three dynamical climate down- scaling methods using the Weather Research and Forecasting (WRF) model’, Journal of Geophysical Research 113(D9).spa
dc.relation.referencesLu, S., Guo, W., Xue, Y., Huang, F. & Ge, J. (2021), ‘Simulation of summer climate over Central Asia shows high sensitivity to different land surface schemes in WRF’, Climate Dynamics 57(7), 2249–2268. URL: https://doi.org/10.1007/s00382-021-05876-9spa
dc.relation.referencesMartin, W. J. & Xue, M. (2006), ‘Sensitivity Analysis of Convection of the 24 May 2002 IHOP Case Using Very Large Ensembles’, Monthly Weather Review 134, 192–207.spa
dc.relation.referencesMartínez-Castro, D., Kumar, S., Flores Rojas, J. L., Moya-Álvarez, A., Valdivia-Prado, J. M., Villalobos-Puma, E., Castillo-Velarde, C. D. & Silva-Vidal, Y. (2019), ‘The Impact of Microphysics Parameterization in the Simulation of Two Convective Rainfall Events over the Central Andes of Peru Using WRF-ARW’, Atmosphere 10(8), 442.spa
dc.relation.referencesMisenis, C. & Zhang, Y. (2010), ‘An examination of sensitivity of WRF/Chem predictions to physical parameterizations, horizontal grid spacing, and nesting options’, Atmospheric Research 97(3), 315–334.spa
dc.relation.referencesMughal, M. O., Lynch, M., Yu, F., McGann, B., Jeanneret, F. & Sutton, J. (2017), ‘Wind modelling, validation and sensitivity study using Weather Research and Forecasting model in complex terrain’, Environmental Modelling and Software 90, 107–125. URL: http://dx.doi.org/10.1016/j.envsoft.2017.01.009spa
dc.relation.referencesNational Centers for Environmental Prediction, National Weather Service, NOAA, U.S. Depart- ment of Commerce (2015), ‘Ncep gfs 0.25 degree global forecast grids historical archive’. URL: https://doi.org/10.5065/D65D8PWKspa
dc.relation.referencesPan, L., Liu, Y., Knievel, J. C., Monache, L. D. & Roux, G. (2018), ‘Evaluations of WRF sensitivities in surface simulations with an ensemble prediction system’, Atmosphere 9(3).spa
dc.relation.referencesPianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B. & Wagener, T. (2016), ‘Sensitivity analysis of environmental models: A systematic review with practical workflow’, Environmental Modelling & Software 79, 214–232.spa
dc.relation.referencesPosada-Marín, J. A., Rendón, A. M., Salazar, J. F., Mejía, J. F. & Villegas, J. C. (2018), ‘WRF downscaling improves ERA-Interim representation of precipitation around a tropical An- dean valley during El Niño: implications for GCM-scale simulation of precipitation over complex terrain’, Climate Dynamics 0(0), 0. URL: http://dx.doi.org/10.1007/s00382-018-4403-0spa
dc.relation.referencesPowers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen, F., Barlage, M. J., Yu, W. & Duda, M. G. (2017), ‘The weather research and forecasting model: Overview, system efforts, and future directions’, Bulletin of the American Meteorological Society 98(8), 1717–1737.spa
dc.relation.referencesReboredo, B., Arasa, R. & Codina, B. (2015), ‘Evaluating Sensitivity to Different Options and Parameterizations of a Coupled Air Quality Modelling System over Bogotá, Colombia. Part I: WRF Model Configuration’, Open Journal of Air Pollution 04(02), 47– 64spa
dc.relation.referencesRey, D. & Neuhäuser, M. (2011), Wilcoxon-signed-rank test, in ‘International encyclopedia of statistical science’, Springer, pp. 1658–1659.spa
dc.relation.referencesRibeiro, L., Caetano, D., Andre, E., Beatriz, S. & Rolim, A. (2021), ‘Reanalysis profile down- scaling with WRF model and sensitivity to PBL parameterization schemes over a subtrop- ical station’, Journal of Atmospheric and Solar-Terrestrial Physics 222(May).spa
dc.relation.referencesRitter, M., Müller, M. D., Jorba, O., Parlow, E. & Liu, L. J. (2013), ‘Impact of chemical and meteorological boundary and initial conditions on air quality modeling: WRF-Chem sen- sitivity evaluation for a European domain’, Meteorology and Atmospheric Physics 119(1- 2), 59–70.spa
dc.relation.referencesSIATA (2022), ‘Siata’. URL: https://siata.gov.co/sitio_web/index.php/spa
dc.relation.referencesSikder, M. S. & Hossain, F. (2018), ‘Sensitivity of initial-condition and cloud microphysics to the forecasting of monsoon rainfall in South Asia’, Meteorological Applications 25(4), 493–509.spa
dc.relation.referencesSolbakken, K. & Birkelund, Y. (2018), ‘Evaluation of the Weather Research and Forecasting (WRF) model with respect to wind in complex terrain’, Journal of Physics: Conference Series 1102spa
dc.relation.referencesSong, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M. & Xu, C. (2015), ‘Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and appli- cations’, Journal of Hydrology 523(225), 739–757. URL: http://dx.doi.org/10.1016/j.jhydrol.2015.02.013spa
dc.relation.referencesSrinivas, C. V., Hari Prasad, K. B., Naidu, C. V., Baskaran, R. & Venkatraman, B. (2016), ‘Sensitivity Analysis of Atmospheric Dispersion Simulations by FLEXPART to the WRF- Simulated Meteorological Predictions in a Coastal Environment’, Pure and Applied Geo- physics 173(2), 675–700spa
dc.relation.referencesTorn, R. D. & Hakim, G. J. (2009), ‘Initial condition sensitivity of Western Pacific extratrop- ical transitions determined using ensemble-based sensitivity analysis’, Monthly Weather Review 137(10), 3388–3406.spa
dc.relation.referencesWang, J. W. A., Sardeshmukh, P. D., Compo, G. P., Whitaker, J. S., Slivinski, L. C., McColl, C. M. & Pegion, P. J. (2019), ‘Sensitivities of the NCEP global forecast system’, Monthly Weather Review 147(4), 1237–1256.spa
dc.relation.referencesWilks, D. S. (2011), Statistical Methods in the Atmospheric Sciences, Elsevier.spa
dc.relation.referencesWu, C., Luo, K., Wang, Q. & Fan, J. (2022), ‘Simulated potential wind power sensitivity to the planetary boundary layer parameterizations combined with various topography datasets in the weather research and forecasting model’, Energy 239, 122047. URL: https://doi.org/10.1016/j.energy.2021.122047spa
dc.relation.referencesYang, B., Qian, Y., Berg, L. K., Ma, P. L., Wharton, S., Bulaevskaya, V., Yan, H., Hou, Z. & Shaw, W. J. (2017), ‘Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model’, Boundary-Layer Meteorology 162(1), 117–142.spa
dc.relation.referencesŽabkar, R., Koracˇin, D. & Rakovec, J. (2013), ‘A WRF/Chem sensitivity study using ensemble modelling for a high ozone episode in Slovenia and the Northern Adriatic area’, Atmo- spheric Environment 77, 990–1004.spa
dc.relation.referencesZack, J., Natenberg, E., Young, S., Knowe, G. V., Waight, K., Manobainco, J. & Kamath, C. (2010), Application of ensemble sensitivity analysis to observation targeting for short-term wind speed forecasting in the Tehachapi region winter season, Technical report, Lawrence Livermore National Laboratoryspa
dc.relation.referencesBreiman, L.(1997).Arcingtheedge.Technicalreport,Tech-nicalReport486,Statis- tics Department,UniversityofCaliforniaat....spa
dc.relation.referencesCampos Murcia,M.R.etal.(2016). Strategiesformodellingbrtmassivetransportationsystems-transmileniocasespa
dc.relation.referencesElsayed, S.,Thyssens,D.,Rashed,A.,Jomaa,H.S.,andSchmidt-Thieme,L.(2021). Do wereallyneeddeeplearningmodelsfortimeseriesforecasting?arXivpreprint arXiv:2101.02118.spa
dc.relation.referencesFriedman, J.H.(2001).Greedyfunctionapproximation:agradientboostingmachine. Annals ofstatistics,pages1189–1232.spa
dc.relation.referencesGriffithDA,ChunY.ImplementingMoraneigenvectorspatialfilteringformassivelylar- ge georeferenceddatasets.InternationalJournalofGeographicalInformationScience. 2019;33(9):1703-1717spa
dc.relation.referencesGriffithDA,Peres-NetoPR.Spatialmodelinginecology:theflexibilityofeigenfunction spatial analyses.Ecology.2006;87(10):2603-2613.spa
dc.relation.referencesGenest, C.,Huang,W.&Dufour,J.-M.(2013),‘Aregularizedgoodness-of-fittestforcopulas’, JournaldelaSociétéfrançaisedestatistique 154(1), 64–77spa
dc.relation.referencesGrønneberg,S.&Hjort,N.L.(2014),‘Thecopulainformationcriteria’, Scandinavian Journal of Statistics 41(2), 436–459.spa
dc.relation.referencesNovales,A.(2017),‘Cópulas’, Madrid: UniversidadComplutense.Departamentode Economía Cuantitativa.Obtenidodehttps://www.ucm.es/data/cont/media/www/pag- 41459/Copulas. pdf .spa
dc.relation.referencesParraArboleda,L.F.(2015),‘Modelamientoconjuntodelnúmerodesiniestrosypagospor reclamación ensegurosmedianteunacópulamixtadesdelaperspectivafrecuentistay bayesiana’, Departamento deEstadística .spa
dc.relation.referencesJolliffe,I.T. (1995). SampleSizesandtheCentralLimitTheorem:ThePoissonDistribution as anIllustration.InTheAmericanStatistician(Vol.49, Issue3,pp.269–269).Informa UK Limited.https://doi.org/10.1080/00031305.1995.10476161spa
dc.relation.referencesCoreTeam. (2022). R:Alanguageandenvironmentforstatisticalcomputing.RFoundation for StatisticalComputing,Vienna,Austria.URLhttps://www.R-project.org/.spa
dc.relation.referencesLudwigFahrmeir,ThomasKneib,StefanLang,BrianMarx.(2013). Regression:Models, Methods andApplications. SpringerScience&BusinessMedia.spa
dc.relation.referencesAndoTomohiro.(2010) Bayesian ModelSelectionandStatisticalModeling. Editorial: Chapman &Hall/CRCisanimprintofTaylor&FrancisGroup,anInformabusiness.spa
dc.relation.referencesDavidL.Weakliem, A CritiqueoftheBayesianInformationCriterionforModelSelection, Sociological Methods&Research,1999,vol.27,issue3,359-397spa
dc.relation.referencesADAMS, FRANCIS K.,ExpertElicitationandBayesianAnalysisofConstructionContract Risks: AnInvestigation. Construction ManagementandEconomics, Vol.24(1),pp.81-96, 2006.spa
dc.relation.referencesCHAZDON ROBIN. AND PEDRO BRANCALION. RestoringForestsasaMeanstoMany Ends. Science, Vol.364(6448).pp.24-25,2019.spa
dc.relation.referencesCORREA-MORALES, JUAN CARLOS. AND BARRERA-CAUSIL, C. IntroducciónaLaEs- tadística Bayesiana. FondoEditorialInstitutoTecnológicoMetropolitano(ITM),2018.spa
dc.relation.referencesCROSSLAND, M. AND ANN, L. AND PAGELLA, T. AND HADGU, K. AND SINCLAIR, F. Implications ofvariationinlocalperceptionofdegradationandrestorationprocessesfor implementing landdegradationneutrality. EnvironmentalDevelopment, Vol.28.pp.42-54, 2018spa
dc.relation.referencesEDRISI SHEIKH ADIL. AND ABHILASH PURUSHOTHAMAN CHIRAKKUZHYIL. Needof TransdisciplinaryResearchforAcceleratingLandRestorationduringtheUNDecadeon Ecosystem Restoration. RestorationEcology, Vol.29(8),2021.spa
dc.relation.referencesESRI ArcGISPro:Versión2.8.0. Redlands, CA:SistemasAmbientalesInstitutodeinves- tigación,2022.spa
dc.relation.referencesFISCHER, JOERN. AND MARAJA RIECHERS. AND JACQUELINE LOOS. AND BERTA MARTIN-LOPEZ. AND VICKY M.TEMPERTON. MakingtheUNDecadeonEcosystem Restoration aSocial-EcologicalEndeavour. TrendsinEcologyandEvolution. Vol.36(1). pp.20-28, 2021spa
dc.relation.referencesHERNÁNDEZ-GÓMEZ, ROSA CATALINA. AND EDGARD CANTILLO-HIGUERA. LaRes- tauración EcológicaComoEstrategiadeConstrucciónSocialEnLaVeredaChipautá,Mu- nicipio deGuaduas,Cundinamarca. Ambiente yDesarrollo. Vol.22(42).pp.1-15,2019.spa
dc.relation.referencesJENKINSON, D. The ElicitationofProbabilities-AReviewoftheStatisticalLiterature. TechnicalReport. Sheffield,UK.,2005.spa
dc.relation.referencesLÓPEZ-GÓMEZ CONNIE. Cartografíasocial:instrumentodegestiónsocialeindicador ambiental. (Tesisdemaestría).UniversidadNacionaldeColombia-SedeMedellín,2012.spa
dc.relation.referencesMETZGER, MORENA MILLS. AND HUGH P.POSSINGHAM. AND RICARDO RIBEIRO RODRIGUES. AND CARLOS ALBERTODE MATTOS SCARAMUZZA. AND FABIO RUBIO SCARANO. AND LEANDRO TAMBOSI. AND MARIA URIARTE. StrategicApproachesto Restoring EcosystemsCanTripleConservationGainsandHalveCosts. NatureEcology and Evolution. Vol.3(1).pp.62-70,2019spa
dc.relation.referencesRCORE TEAM. R: Alanguageandenvironmentforstatisticalcomputing.RFoundation for StatisticalComputing. Vienna,Austria,2022.spa
dc.relation.referencesRAMÍREZ, CRISTIAN Determinantes EspacialmenteExplícitosdeTransicionesEnCo- berturasTerrestresConSignificativoImpactoParaLaProvisióndeServiciosEcosistémi- cos: AnálisisTemporalyEspacial. (TesisdeMaestría).UniversidadNacionaldeColombia - SedeMedellín,2014spa
dc.relation.referencesSTRASSBURG, BERNARDO B.N. AND ALVARO IRIBARREM. AND HAWTHORNE L. BEYER. AND CARLOS LEANDRO CORDEIRO. AND RENATO CROUZEILLES. AND CATARINA C.JAKOVAC. AND ANDRÉ BRAGA JUNQUEIRA. AND EDUARDO LACERDA. AND AGNIESZKA E.LATAWIEC. AND ANDREW BALMFORD. AND THOMAS M.BROOKS. AND STUART H.M.BUTCHART. AND ROBIN L.CHAZDON. AND KARL HEINZ ERB. AND PEDRO BRANCALION. AND GRAEME BUCHANAN. AND DAVID COOPER. AND SANDRA DÍAZ. AND PAUL F.DONALD. AND VALERIE KAPOS. AND DAVID LECLÈRE. AND LERA MILES. AND MICHAEL OBERSTEINER. AND CHRISTOPH PLUTZAR. AND CARLOS ALBERTO CARLOS. AND FABIO R.SCARANO. AND PIERO VISCONTI. Global Priority AreasforEcosystemRestoration. Nature. Vol.586(7831).pp.724-29,2020.spa
dc.relation.referencesELISE CASPERSEN, MARIO ARRIETA-PRIETO & XIAOKUN (CARA) WANG (2021), Latent splitofaggregatecounts:revealinghomedeliveriespercommoditytypes and potentialfreighttripimplications, TransportmetricaA:TransportScience,DOI: 10.1080/23249935.2021.1990438.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.lembMEMORIA-CONGRESOS, CONFERENCIAS, ETC.
dc.subject.lembMemory - Congresses
dc.subject.lembANALISIS DE REGRESION LOGISTICA
dc.subject.lembLogistic regression analysis
dc.subject.lembAPRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL)
dc.subject.lembMachine learning
dc.subject.lembANALISIS MULTIVARIANTE
dc.subject.lembMultivariate analysis
dc.subject.proposalModelos bayesianosspa
dc.subject.proposalRegresión logísticaspa
dc.subject.proposalAlgoritmos de machine learningspa
dc.subject.proposalMétodos no paramétricosspa
dc.subject.proposalAnálisis multivariadospa
dc.subject.proposalModelos de conteo Poisson y ZIPspa
dc.subject.proposalProcesos espaciales y espaciotemporalesspa
dc.subject.proposalMétodos de clasificaciónspa
dc.subject.proposalPruebas de normalidadspa
dc.subject.proposalRedes neuronalesspa
dc.title31° Simposio Internacional de estadística 2022 : múltiples temáticasspa
dc.typeLibrospa
dc.type.coarhttp://purl.org/coar/resource_type/c_2f33spa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bookspa
dc.type.redcolhttp://purl.org/redcol/resource_type/LIBspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentEstudiantesspa
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