A Nonparametric Approach Using Dirichlet Process for Hierarchical Generalized Linear Mixed Models

Autor:	Jing Wang
Rok vydání:	2021
Předmět:	Hierarchical Dirichlet process Dirichlet process symbols.namesake Mathematical optimization Generalized Dirichlet distribution Categorical distribution symbols Concentration parameter Applied mathematics Dirichlet distribution Generalized linear mixed model Gibbs sampling Mathematics
Zdroj:	Journal of Data Science. 8:43-59
ISSN:	1683-8602 1680-743X
DOI:	10.6339/jds.2010.08(1).562
Popis:	In this paper, we propose a nonparametric approach using the Dirichlet processes (DP) as a class of prior distributions for the distribution G of the random effects in the hierarchical generalized linear mixed model (GLMM). The support of the prior distribution (and the posterior distribution) is large, allowing for a wide range of shapes for G. This provides great flexibility in estimating G and therefore produces a more flexible estimator than does the parametric analysis. We present some computation strategies for posterior computations involved in DP modeling. The proposed method is illustrated with real examples as well as simulations.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b22a4dc2e18e142aec7993ec7066f1b5 https://doi.org/10.6339/jds.2010.08(1).562 Zobrazit plný text záznamu