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Type: Artigo de periódico
Title: A non-iterative sampling Bayesian method for linear mixed models with normal independent distributions
Author: Lachos, VH
Cabral, CRB
Abanto-Valle, CA
Abstract: In this paper, we utilize normal/independent (NI) distributions as a tool for robust modeling of linear mixed models (LMM) under a Bayesian paradigm. The purpose is to develop a non-iterative sampling method to obtain i.i.d. samples approximately from the observed posterior distribution by combining the inverse Bayes formulae, sampling/importance resampling and posterior mode estimates from the expectation maximization algorithm to LMMs with NI distributions, as suggested by Tan et al. [33]. The proposed algorithm provides a novel alternative to perfect sampling and eliminates the convergence problems of Markov chain Monte Carlo methods. In order to examine the robust aspects of the NI class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on model selection criteria are given. The new methodologies are exemplified through a real data set, illustrating the usefulness of the proposed methodology.
Subject: Gibbs algorithms
inverse Bayes formulae
linear mixed models
normal/independent distributions
sampling/importance resampling
Country: Inglaterra
Editor: Taylor & Francis Ltd
Rights: fechado
Identifier DOI: 10.1080/02664763.2011.603292
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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