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dc.rights.licenseAtribución-NoComercial 4.0 Internacional
dc.contributorCepeda Cuervo, Edilberto
dc.contributor.authorGarrido Lopera, Bertha Liliana
dc.date.accessioned2019-06-24T16:57:30Z
dc.date.available2019-06-24T16:57:30Z
dc.date.issued2010
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/7854
dc.description.abstractSe 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.
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.relation.ispartofUniversidad Nacional de Colombia Sede Bogotá Facultad de Ciencias Departamento de Estadística
dc.relation.ispartofDepartamento de Estadística
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subject.ddc31 Colecciones de estadística general / Statistics
dc.titleA generalization of Bayesian estimation in finite mixture of distributions
dc.typeTrabajo de grado - Doctorado
dc.type.driverinfo:eu-repo/semantics/doctoralThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.identifier.eprintshttp://bdigital.unal.edu.co/4313/
dc.description.degreelevelDoctorado
dc.relation.referencesGarrido Lopera, Bertha Liliana (2010) A generalization of Bayesian estimation in finite mixture of distributions. Doctorado thesis, Universidad Nacional de Colombia.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalMetodologa bayesiana
dc.subject.proposalMixtura finita de distribuciones
dc.subject.proposalFamilia exponencial biparamtrica
dc.subject.proposalFamilia weibull biparamtrica / Bayesian methodology
dc.subject.proposalFinite mixture of distributions
dc.subject.proposalBiparametric exponential family
dc.subject.proposalBiparametric weibull family
dc.type.coarhttp://purl.org/coar/resource_type/c_db06
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TD
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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Atribución-NoComercial 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