THE MODES OF POSTERIOR DISTRIBUTIONS FOR MIXED LINEAR MODELS

dc.creatorCARRIQUIRY,ALICIA L
dc.creatorKLIEMANN,WOLFGANG
dc.date2007-12-01
dc.date.accessioned2017-03-07T15:58:16Z
dc.date.available2017-03-07T15:58:16Z
dc.identifierhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-09172007000300006
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/393767
dc.descriptionMixed 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.
dc.formattext/html
dc.languageen
dc.publisherUniversidad Católica del Norte, Departamento de Matemáticas
dc.sourceProyecciones (Antofagasta) v.26 n.3 2007
dc.subjectPosterior modes
dc.subjectmixed linear models
dc.subjectpoly-t distributions
dc.titleTHE MODES OF POSTERIOR DISTRIBUTIONS FOR MIXED LINEAR MODELS
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución