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Modelos lineales doblemente generalizados con enfoque bayesiano: teoría y aplicaciones en R, OpenBUGS y Stan

dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.advisorCepeda Cuervo, Edilberto
dc.contributor.authorÁvila Perico, Hernan David
dc.date.accessioned2021-01-29T15:10:23Z
dc.date.available2021-01-29T15:10:23Z
dc.date.issued2020-09-15
dc.identifier.citationÁvila Perico, H. D. (2020). Modelos lineales doblemente generalizados con enfoque bayesiano: teoria y aplicaciones en R, OpenBUGS y Stan [Tesis de maestría, Universidad Nacional de Colombia]. Repositorio Institucional.
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/78992
dc.description.abstractThis work presents Double Generalized Linear Models as a technique which allows data modeling when there is variable dispersion. This document contains theoretical aspects and details about bayesian estimation using Stan and OpenBUGS programming languages through simulations and applications developed in R using package R2OpenBUGS and rstan distribution. This document contains examples for heteroscedastic normal regression, gamma regression, beta regression with simulations and applications. Furthermore, models for count data based on beta binomial and negative binomial distribution are presented in chapter 3. Finally an extension for longitudinal data based on multivariate normal distribution is exposed. The expectancy is this document to be a guide for understanding and use of this model in a bayesian context.
dc.description.abstractEn este trabajo se presentan los Modelos Lineales Doblemente Generalizados como una técnica que permite en modelamiento de datos cuando existe dispersióon variable. Este documento presenta consideraciones teóricas y aspectos relacionados a la estimación bayesiana en los lenguajes Stan y OpenBUGS a través de simulaciones y aplicaciones desarrolladas en R usando el paquete R2OpenBUGS y la distribución rstan. Se presentan ejemplos de DGLM como la regresión normal heteroscedástica, la regresión gamma, la regresión beta con simulaciones y aplicaciones. Además, en el capitulo 3 se plantean modelos para datos de conteo basados en la distribución beta binomial y binomial negativa y finalmente, se presenta una extensión para modelamiento de datos longitudinales basados en la distribución normal multivariada. Se espera que este documento sirva de guía para la comprensión y utilización de estos modelos en un contexto bayesiano.
dc.format.extent162
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc510 - Matemáticas
dc.titleModelos lineales doblemente generalizados con enfoque bayesiano: teoría y aplicaciones en R, OpenBUGS y Stan
dc.typeOtro
dc.rights.spaAcceso abierto
dc.description.additionalLínea de Investigación: Inferencia Bayesiana.
dc.type.driverinfo:eu-repo/semantics/other
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Estadística
dc.contributor.researchgroupINFERENCIA BAYESIANA
dc.description.degreelevelMaestría
dc.publisher.departmentDepartamento de Estadística
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalDGLM
dc.subject.proposalOpen-BUGS
dc.subject.proposalOpenBUGS
dc.subject.proposalModelos lineales doblemente generalizados
dc.subject.proposalDouble generalized lineas models
dc.subject.proposalRegresión normal heteroscedástica
dc.subject.proposalRegresión gamma
dc.subject.proposalStan
dc.subject.proposalRegresión beta
dc.subject.proposalHeteroscedastic normal regression
dc.subject.proposalRegresión beta binomial
dc.subject.proposalGamma regression
dc.subject.proposalBeta regression
dc.subject.proposalRegresión binomial negativa
dc.subject.proposalBeta binomial regression
dc.subject.proposalDatos longitudinales
dc.subject.proposalNegative binomial regression
dc.subject.proposalSobredispersión
dc.subject.proposalDispersión variable
dc.subject.proposalLogitudinal data
dc.subject.proposalVariable dispersion
dc.subject.proposalStan
dc.subject.proposalOverdispersion
dc.type.coarhttp://purl.org/coar/resource_type/c_1843
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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