Modelos lineales doblemente generalizados con enfoque bayesiano: teoría y aplicaciones en R, OpenBUGS y Stan
| dc.contributor.advisor | Cepeda Cuervo, Edilberto | spa |
| dc.contributor.author | Ávila Perico, Hernan David | spa |
| dc.contributor.researchgroup | INFERENCIA BAYESIANA | spa |
| dc.date.accessioned | 2021-01-29T15:10:23Z | spa |
| dc.date.available | 2021-01-29T15:10:23Z | spa |
| dc.date.issued | 2020-09-15 | spa |
| dc.description.abstract | This 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. | spa |
| dc.description.abstract | En 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. | spa |
| dc.description.additional | Línea de Investigación: Inferencia Bayesiana. | spa |
| dc.description.degreelevel | Maestría | spa |
| dc.format.extent | 162 | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.citation | Ávila 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. | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/78992 | |
| dc.language.iso | spa | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
| dc.publisher.department | Departamento de Estadística | spa |
| dc.publisher.program | Bogotá - Ciencias - Maestría en Ciencias - Estadística | spa |
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| dc.rights | Derechos reservados - Universidad Nacional de Colombia | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
| dc.rights.spa | Acceso abierto | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
| dc.subject.ddc | 510 - Matemáticas | spa |
| dc.subject.proposal | DGLM | eng |
| dc.subject.proposal | Open-BUGS | spa |
| dc.subject.proposal | OpenBUGS | eng |
| dc.subject.proposal | Modelos lineales doblemente generalizados | spa |
| dc.subject.proposal | Double generalized lineas models | eng |
| dc.subject.proposal | Regresión normal heteroscedástica | spa |
| dc.subject.proposal | Regresión gamma | spa |
| dc.subject.proposal | Stan | eng |
| dc.subject.proposal | Regresión beta | spa |
| dc.subject.proposal | Heteroscedastic normal regression | eng |
| dc.subject.proposal | Regresión beta binomial | spa |
| dc.subject.proposal | Gamma regression | eng |
| dc.subject.proposal | Beta regression | eng |
| dc.subject.proposal | Regresión binomial negativa | spa |
| dc.subject.proposal | Beta binomial regression | eng |
| dc.subject.proposal | Datos longitudinales | spa |
| dc.subject.proposal | Negative binomial regression | eng |
| dc.subject.proposal | Sobredispersión | spa |
| dc.subject.proposal | Dispersión variable | spa |
| dc.subject.proposal | Logitudinal data | eng |
| dc.subject.proposal | Variable dispersion | eng |
| dc.subject.proposal | Stan | spa |
| dc.subject.proposal | Overdispersion | eng |
| dc.title | Modelos lineales doblemente generalizados con enfoque bayesiano: teoría y aplicaciones en R, OpenBUGS y Stan | spa |
| dc.type | Trabajo de grado - Maestría | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |