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dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.advisorArunachalam, Viswanathan
dc.contributor.authorRamirez Yara, Yessica Natalia
dc.date.accessioned2024-05-08T21:24:35Z
dc.date.available2024-05-08T21:24:35Z
dc.date.issued2023-05-24
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/86057
dc.descriptionilustraciones, diagramas
dc.description.abstractEl documento a continuación tiene como objetivo estimar modelos estocásticos de series de tiempo ARIMA para las toneladas de especies de peces más importantes a nivel social y económico en el Pacífico colombiano, capturadas por medio de pesca. Se pretende usar como covariables de los modelos de producción por especie, las curvas de salinidad y temperatura medidas a -0.5, -41 y -86 metros bajo la superficie del océano, las cuales fueron calculadas usando herramientas de análisis espacial y análisis de datos funcionales, tomando como referencia para cada especie, el área formada por los pixeles con los valores más altos de probabilidad del ráster de presencia de especies y los archivos NetCDF del Pacífico de temperatura y salinidad para distintas profundidades; con el fin de explicar el comportamiento de la producción de desembarcos por especie en función de los cambios de las variables oceanográficas. (Texto tomado de la fuente)
dc.description.abstractThe following document aims to estimate stochastic ARIMA time series models for the production in tons of fishing landings of the most socially and economically important fish species in the Colombian Pacific. It is intended to use as covariates of the production models by species, the salinity and temperature curves measured at -0.5, -41 and -86 meters under the ocean surface, which were calculated using spatial analysis tools and functional data analysis, taking as a reference for each species, the area formed by the pixels with the highest probability values of the species presence raster and the Pacific NetCDF files of temperature and salinity for different depths; in order to be able to explain the behavior of the production of landings by species as a function of changes in oceanographic variables.
dc.format.extentxiv, 74 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
dc.titleModelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales
dc.typeTrabajo de grado - Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Estadística
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ciencias - Estadística
dc.description.researchareaAnálisis espacial, Series de tiempo, Datos funcionales
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.facultyFacultad de Ciencias
dc.publisher.placeBogotá, Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
dc.relation.references[Aguilera and Aguilera-Morillo, 2013] Aguilera, A. and Aguilera-Morillo, M. (2013). Comparative study of different b-spline approaches for functional data. Mathematical and Computer Modelling, 58(7):1568–1579.
dc.relation.references[Bohn, 2005] Bohn, M. (2005). Univariate time series analysis;arima models. Econometrics 2.
dc.relation.references[Boh´orquez et al., 2016] Boh´orquez, M., Giraldo, R., and Mateu, J. (2016). Optimal sampling for spatial prediction of functional data. Statistical Methods & Applications, 25(1):39–54.
dc.relation.references[Box et al., 2015] Box, G., Jenkins, G., Reinsel, G., and Ljung, G. (2015). Time Series Analysis: Forecasting and Control. Wiley Series in Probability and Statistics. Wiley.
dc.relation.references[Brockwell and Davis, 1996] Brockwell, P. J. and Davis, R. A. (1996). Introduction to time series and forecasting / Peter J. Brockwell and Richard A. Davis. Springer New York.
dc.relation.references[Campos et al., 2016] Campos, R., Cremona, M., Pini, A., Chiaromonte, F., and Makova, K. (2016). Integration and fixation preferences of human and mouse endogenous retroviruses uncovered with functional data analysis. . PLoS Comput Biol, 12(6).
dc.relation.references[de Rivera, 1989] de Rivera, D. (1989). Estad´ıstica: Modelos y M´etodos. Alianza Universidad Textos Series. Alianza Editorial, S. A.
dc.relation.references[FAO, 2017] FAO (2017). Food and agriculture organization of the united nations.
dc.relation.references[Fortin, 2015] Fortin, D. (2015). Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework. Tesis de doctorado, Iowa State University.
dc.relation.references[Ghose, 2018] Ghose, R. (2018). 1.29 - defining public participation gis. In Huang, B., editor, Comprehensive Geographic Information Systems, pages 431–437. Elsevier, Oxford.
dc.relation.references[Goodchild, 2009] Goodchild, M. (2009). Geographic information systems and science: Today and tomorrow. Procedia Earth and Planetary Science, 1:1037–1043.
dc.relation.references[Hamza et al., 2021] Hamza, F., Valsala, V., Mallissery, A., and George, G. (2021). Climate impacts on the landings of indian oil sardine over the south-eastern arabian sea. Fish and Fisheries, 22(1):175–193.
dc.relation.references[Herrera Montiel et al., 2019] Herrera Montiel, S. A., Coronado-Franco, K. V., and Selvaraj, J. J. (2019). Predicted changes in the potential distribution of seerfish (scomberomorus sierra) under multiple climate change scenarios in the colombian pacific ocean. Ecological Informatics, 53:100985.
dc.relation.references[Hormann et al., 2015] Hormann, S., Kidzinski, L., and Hallin, M. (2015). Dynamic functional principal components. Journal of the Royal Statistical Society: Series B: Statistical Methodology, 319-348.
dc.relation.references[Horv´ath and Kokoszka, 2012] Horv´ath, L. and Kokoszka, P. (2012). Inference for Functional Data with Applications. Springer Series in Statistics. Springer New York.
dc.relation.references[Hyndman and Ullah, 2007] Hyndman, R. and Ullah, M. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics & Data Analysis, 51(10):4942–4956.
dc.relation.references[Kuenzer et al., 2021] Kuenzer, T., H¨ormann, S., and Kokoszka, P. (2021). Principal component analysis of spatially indexed functions. Journal of the American Statistical Association, 116(535), 1444-1456.
dc.relation.references[Lewis, 1980] Lewis, E. (1980). The practical salinity scale 1978 and its antecedents. IEEE J. Ocean. Eng, OE-5(1): 3-8.
dc.relation.references[Liu et al., 2016] Liu, C., Ray, S., and Hooker, G. (2016). Functional principal component analysis of spatially correlated data. Statistics and Computing, 1:1–16.
dc.relation.references[Makridakis et al., 1983] Makridakis, S., Wheelwright, S., and McGee, V. (1983). Forecasting: Methods and Applications. Wiley series in management. Wiley.
dc.relation.references[Marco et al., 2021] Marco, J., Valderrama, D., and Rueda, M. (2021). Evaluating management reforms in a colombian shrimp fishery using fisheries performance indicators. Marine Policy, 125:104258.
dc.relation.references[Martínez, 2020] Martínez, E. (2020). Un modelo estocástico para analizar los efectos de la variación de la temperatura sobre la captura pesquera a lo largo de la costa del Pacífico Colombiano. Master’s thesis, Universidad Nacional de Colombia.
dc.relation.references[Montgomery et al., 2015] Montgomery, D. C., Jennings, C. L., and Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
dc.relation.references[Moriarity et al., 2020] Moriarity, R. J., Liberda, E. N., and Tsuji, L. J. (2020). Using a geographic information system to assess local scale methylmercury exposure from fish in nine communities of the eeyou istchee territory (james bay, quebec, canada). Environmental Research, 191:110147.
dc.relation.references[Nieto and Mélin, 2017] Nieto, K. and Mélin, F. (2017). Variability of chlorophyll-a concentration in the gulf of guinea and its relation to physical oceanographic variables. Progress in Oceanography, 151:97–115.
dc.relation.references[Ojeda and Arias, 2000] Ojeda, L. and Arias, R. (2000). Informe nacional sobre la gestión de agua en colombia (recursos hídricos, agua potable y saneamiento). Ministerio de Medio Ambiente, Santafé de Bogotá, page 137.
dc.relation.references[Pawlowicz, 2013] Pawlowicz, R. (2013). Key physical variables in the ocean: Temperature, salinity, and density. Nature Education Knowledge, 4(4):13.
dc.relation.references[Ramsay, 2006] Ramsay, J. (2006). Functional data analysis. Springer New York.
dc.relation.references[Ramsay and Silverman, 2005] Ramsay, J. and Silverman, B. (2005). Functional Data Analysis. Springer Series in Statistics. Springer.
dc.relation.references[Santos et al., 2020] Santos, E. F., Barbosa, A. L., and Duarte-Neto, P. J. (2020). Global correlation matrix spectra of the surface temperature of the oceans from random matrix theory to poisson fluctuations. Physics Letters A, 384(27):126689.
dc.relation.references[Selvaraj et al., 2017] Selvaraj, J., Coronado-Franco, K., and Guzm´an, A. (2017). Caracterización espacial y temporal de la salinidad y temperatura en bancos de pesca del océano pacífico colombiano. Congreso Latinoamericano de Ciencias del Mar - COLACMAR 2017.
dc.relation.references[Selvaraj et al., 2018] Selvaraj, J., Coronado-Franco, K., and Guzmán, A. (2018). Projected sea surface temperature changes in the fishing areas of the colombian pacific under climate change scenarios. 4th International symposium: The effects of climate change on the World´s Oceans.
dc.relation.references[Selvaraj et al., 2020] Selvaraj, J. J., Arunachalam, V., Coronado-Franco, K. V., RomeroOrjuela, L. V., and Ramírez-Yara, Y. N. (2020). Time-series modeling of fishery landings in the colombian pacific ocean using an arima model. Regional Studies in Marine Science, 39:101477.
dc.relation.references[Shang and Kearney, 2022] Shang, H. L. and Kearney, F. (2022). Dynamic functional timeseries forecasts of foreign exchange implied volatility surfaces. International Journal of Forecasting, 38(3):1025–1049.
dc.relation.references[Van den Bossche et al., 2007] Van den Bossche, F., Wets, G., and Brijs, T. (2007). A regression model with arima errors to investigate the frequency and severity of road traffic accidents.
dc.relation.references[Venkatramanan et al., 2019] Venkatramanan, S., Prasanna, M., and Chung, S. (2019). GIS and Geostatistical Techniques for Groundwater Science. Elsevier.
dc.relation.references[Wang et al., 2016] Wang, J., Chiou, J., and M¨uller, H. (2016). Functional data analysis. Annual Review of Statistics and Its Application, 3(1):257–295.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.lembANALISIS FUNCIONAL
dc.subject.lembFunctional analysis
dc.subject.proposalDatos funcionales
dc.subject.proposalAnálisis espacial
dc.subject.proposalDesembarcos pesqueros
dc.subject.proposalSeries de tiempo
dc.subject.proposalModelos ARIMA con covariables
dc.subject.proposalFunctional data analysis
dc.subject.proposalSpatial analysis
dc.subject.proposalFish landings
dc.subject.proposalTime series
dc.subject.proposalARIMA models with covariates
dc.title.translatedSpatiotemporal modeling of fishing landings in the Colombian Pacific using functional data
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
dcterms.audience.professionaldevelopmentInvestigadores
dc.contributor.orcidRamirez Yara, Yessica Natalia [0000000196345406]
dc.contributor.cvlacRAMIREZ YARA, YESSICA NATALIA [0000094819]
dc.contributor.researchgateRamirez Yara, Yessica [Yessica-Ramirez-Yara]


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Atribución-NoComercial-SinDerivadas 4.0 InternacionalThis work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit