A generalization of Bayesian estimation in finite mixture of distributions
Se emplea metodologa bayesiana, especficamente el muestreador de Gibbs y el algoritmo de Metropolis-Hastings, para estimar los parmetros en una mixtura finita de distribuciones pertenecientes a la familia exponencial biparamtrica, o a la familia de weibull biparamtrica, modelando media y varianza de...
- Autores:
-
Garrido Lopera, Bertha Liliana
- Tipo de recurso:
- Doctoral thesis
- Fecha de publicación:
- 2010
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/7854
- Palabra clave:
- 31 Colecciones de estadística general / Statistics
Metodologa bayesiana
Mixtura finita de distribuciones
Familia exponencial biparamtrica
Familia weibull biparamtrica / Bayesian methodology
Finite mixture of distributions
Biparametric exponential family
Biparametric weibull family
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Atribución-NoComercial 4.0 InternacionalDerechos reservados - Universidad Nacional de Colombiahttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cepeda Cuervo, EdilbertoGarrido Lopera, Bertha Liliana7bd5fa81-ff9f-4821-8781-4d972e75f4cc3002019-06-24T16:57:30Z2019-06-24T16:57:30Z2010https://repositorio.unal.edu.co/handle/unal/7854http://bdigital.unal.edu.co/4313/Se emplea metodologa bayesiana, especficamente el muestreador de Gibbs y el algoritmo de Metropolis-Hastings, para estimar los parmetros en una mixtura finita de distribuciones pertenecientes a la familia exponencial biparamtrica, o a la familia de weibull biparamtrica, modelando media y varianza de las distribuciones involucradas. En una mixtura de k distribuciones hay m de un familia de distribuciones y k − m de otra familia de distribuciones, m = 0, . . . , k. Las distribuciones que se trabajaron en los algoritmos fueron especficamente, normal y exponencial, normal y gama, y normal y weibull. La media y la varianza se modelaron con regresiones lineales y no lineales con un nmero arbitrario de covariables. Se aplic la metodologa bayesiana a la mixtura finita para modelar ejemplos tpicos de la estadstica espacial y de los modelos TAR de series de tiempo no lineales. / Abstract. Bayesian methodology is employed, mainly the Gibbs sampler and theMetropolis- Hastings algorithm, to estimate the parameters in a finite mixture of distributions belonging to the exponential biparametric family, or the biparametric weibull family of distributions, modeling the mean and the variance of all the distributions involved. In a mixture consisting of k distributions, there are m from one family and k−m from another family, m = 0, . . . , k. The algorithms worked with distributions from the normal and exponential families, normal and gamma families, and normal and weibull families. The mean and the variance, with an arbitrary number of covariates, were modelled with linear and non linear regressions. Bayesian methodology was applied to finite mixtures to model typical examples from spatial statistics and from non linear time series TAR models.Doctoradoapplication/pdfspaUniversidad Nacional de Colombia Sede Bogotá Facultad de Ciencias Departamento de EstadísticaDepartamento de EstadísticaGarrido Lopera, Bertha Liliana (2010) A generalization of Bayesian estimation in finite mixture of distributions. Doctorado thesis, Universidad Nacional de Colombia.31 Colecciones de estadística general / StatisticsMetodologa bayesianaMixtura finita de distribucionesFamilia exponencial biparamtricaFamilia weibull biparamtrica / Bayesian methodologyFinite mixture of distributionsBiparametric exponential familyBiparametric weibull familyA generalization of Bayesian estimation in finite mixture of distributionsTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TDORIGINAL01-196914.2010.pdfapplication/pdf1519528https://repositorio.unal.edu.co/bitstream/unal/7854/1/01-196914.2010.pdf217fcaa94c676a4f060f9d3ae5ff8887MD51THUMBNAIL01-196914.2010.pdf.jpg01-196914.2010.pdf.jpgGenerated Thumbnailimage/jpeg4661https://repositorio.unal.edu.co/bitstream/unal/7854/2/01-196914.2010.pdf.jpg74afe98fa2f2cb825f65e7e5abf6897bMD52unal/7854oai:repositorio.unal.edu.co:unal/78542023-08-29 23:04:56.557Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.co |
dc.title.spa.fl_str_mv |
A generalization of Bayesian estimation in finite mixture of distributions |
title |
A generalization of Bayesian estimation in finite mixture of distributions |
spellingShingle |
A generalization of Bayesian estimation in finite mixture of distributions 31 Colecciones de estadística general / Statistics Metodologa bayesiana Mixtura finita de distribuciones Familia exponencial biparamtrica Familia weibull biparamtrica / Bayesian methodology Finite mixture of distributions Biparametric exponential family Biparametric weibull family |
title_short |
A generalization of Bayesian estimation in finite mixture of distributions |
title_full |
A generalization of Bayesian estimation in finite mixture of distributions |
title_fullStr |
A generalization of Bayesian estimation in finite mixture of distributions |
title_full_unstemmed |
A generalization of Bayesian estimation in finite mixture of distributions |
title_sort |
A generalization of Bayesian estimation in finite mixture of distributions |
dc.creator.fl_str_mv |
Garrido Lopera, Bertha Liliana |
dc.contributor.author.spa.fl_str_mv |
Garrido Lopera, Bertha Liliana |
dc.contributor.spa.fl_str_mv |
Cepeda Cuervo, Edilberto |
dc.subject.ddc.spa.fl_str_mv |
31 Colecciones de estadística general / Statistics |
topic |
31 Colecciones de estadística general / Statistics Metodologa bayesiana Mixtura finita de distribuciones Familia exponencial biparamtrica Familia weibull biparamtrica / Bayesian methodology Finite mixture of distributions Biparametric exponential family Biparametric weibull family |
dc.subject.proposal.spa.fl_str_mv |
Metodologa bayesiana Mixtura finita de distribuciones Familia exponencial biparamtrica Familia weibull biparamtrica / Bayesian methodology Finite mixture of distributions Biparametric exponential family Biparametric weibull family |
description |
Se emplea metodologa bayesiana, especficamente el muestreador de Gibbs y el algoritmo de Metropolis-Hastings, para estimar los parmetros en una mixtura finita de distribuciones pertenecientes a la familia exponencial biparamtrica, o a la familia de weibull biparamtrica, modelando media y varianza de las distribuciones involucradas. En una mixtura de k distribuciones hay m de un familia de distribuciones y k − m de otra familia de distribuciones, m = 0, . . . , k. Las distribuciones que se trabajaron en los algoritmos fueron especficamente, normal y exponencial, normal y gama, y normal y weibull. La media y la varianza se modelaron con regresiones lineales y no lineales con un nmero arbitrario de covariables. Se aplic la metodologa bayesiana a la mixtura finita para modelar ejemplos tpicos de la estadstica espacial y de los modelos TAR de series de tiempo no lineales. / Abstract. Bayesian methodology is employed, mainly the Gibbs sampler and theMetropolis- Hastings algorithm, to estimate the parameters in a finite mixture of distributions belonging to the exponential biparametric family, or the biparametric weibull family of distributions, modeling the mean and the variance of all the distributions involved. In a mixture consisting of k distributions, there are m from one family and k−m from another family, m = 0, . . . , k. The algorithms worked with distributions from the normal and exponential families, normal and gamma families, and normal and weibull families. The mean and the variance, with an arbitrary number of covariates, were modelled with linear and non linear regressions. Bayesian methodology was applied to finite mixtures to model typical examples from spatial statistics and from non linear time series TAR models. |
publishDate |
2010 |
dc.date.issued.spa.fl_str_mv |
2010 |
dc.date.accessioned.spa.fl_str_mv |
2019-06-24T16:57:30Z |
dc.date.available.spa.fl_str_mv |
2019-06-24T16:57:30Z |
dc.type.spa.fl_str_mv |
Trabajo de grado - Doctorado |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_db06 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TD |
format |
http://purl.org/coar/resource_type/c_db06 |
status_str |
acceptedVersion |
dc.identifier.uri.none.fl_str_mv |
https://repositorio.unal.edu.co/handle/unal/7854 |
dc.identifier.eprints.spa.fl_str_mv |
http://bdigital.unal.edu.co/4313/ |
url |
https://repositorio.unal.edu.co/handle/unal/7854 http://bdigital.unal.edu.co/4313/ |
dc.language.iso.spa.fl_str_mv |
spa |
language |
spa |
dc.relation.ispartof.spa.fl_str_mv |
Universidad Nacional de Colombia Sede Bogotá Facultad de Ciencias Departamento de Estadística Departamento de Estadística |
dc.relation.references.spa.fl_str_mv |
Garrido Lopera, Bertha Liliana (2010) A generalization of Bayesian estimation in finite mixture of distributions. Doctorado thesis, Universidad Nacional de Colombia. |
dc.rights.spa.fl_str_mv |
Derechos reservados - Universidad Nacional de Colombia |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.rights.license.spa.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Atribución-NoComercial 4.0 Internacional Derechos reservados - Universidad Nacional de Colombia http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
institution |
Universidad Nacional de Colombia |
bitstream.url.fl_str_mv |
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Repositorio Institucional Universidad Nacional de Colombia |
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