Semiparametric Bayes hierarchical models with mean and variance constraints.

Published

Journal Article

In parametric hierarchical models, it is standard practice to place mean and variance constraints on the latent variable distributions for the sake of identifiability and interpretability. Because incorporation of such constraints is challenging in semiparametric models that allow latent variable distributions to be unknown, previous methods either constrain the median or avoid constraints. In this article, we propose a centered stick-breaking process (CSBP), which induces mean and variance constraints on an unknown distribution in a hierarchical model. This is accomplished by viewing an unconstrained stick-breaking process as a parameter-expanded version of a CSBP. An efficient blocked Gibbs sampler is developed for approximate posterior computation. The methods are illustrated through a simulated example and an epidemiologic application.

Full Text

Duke Authors

Cited Authors

  • Yang, M; Dunson, DB; Baird, D

Published Date

  • September 2010

Published In

Volume / Issue

  • 54 / 9

Start / End Page

  • 2172 - 2186

PubMed ID

  • 24363478

Pubmed Central ID

  • 24363478

Electronic International Standard Serial Number (EISSN)

  • 1872-7352

International Standard Serial Number (ISSN)

  • 0167-9473

Digital Object Identifier (DOI)

  • 10.1016/j.csda.2010.03.025

Language

  • eng