The modes of posterior distributions for mixed linear models Carriquiry, Alicia Kliemann, Wolfgang Carriquiry, Alicia
dc.contributor.department Statistics 2018-02-17T06:12:13.000 2020-07-02T06:57:16Z 2020-07-02T06:57:16Z Mon Jan 01 00:00:00 UTC 2007 2007-01-01
dc.description.abstract <p>Mixed linear models, also known as two-level hierarchical models, are commonly used in many applications. In this paper, we consider the marginal distribution that arises within a Bayesian framework, when the components of variance are integrated out of the joint posterior distribution. We provide analytical tools for describing the surface of the distribution of interest. The main theorem and its proof show how to determine the number of local maxima, and their approximate location and relative size. This information can be used by practitioners to assess the performance of Laplace-type integral approximations, to compute possibly disconnected highest posterior density regions, and to custom-design numerical algorithms.</p>
dc.description.comments <p>This article is from <em>Proyecciones</em> 26 (2007): 281, doi:<a href="" target="_blank">10.4067/S0716-09172007000300006</a>. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1020
dc.identifier.contextkey 7849960
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/22
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 22:40:44 UTC 2022
dc.source.uri 10.4067/S0716-09172007000300006
dc.subject.disciplines Statistics and Probability
dc.subject.keywords mixed linear models
dc.subject.keywords posterior modes
dc.subject.keywords poly-t distributions
dc.subject.keywords Pontificia Universidad Catolica de Chile
dc.title The modes of posterior distributions for mixed linear models
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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