Bayesian Analysis for Random Effects Models

Junshan Shen and Catherine Chunling Liu

A chapter in Bayesian Inference on Complicated Data from IntechOpen

Abstract: Random effects models have been widely used to analyze correlated data sets, and Bayesian techniques have emerged as a powerful tool to fit the models. However, there has been scarce literature that systematically reviews and summarizes the recent advances of Bayesian analyses of random effects models. This chapter reviews the use of the Dirichlet process mixture (DPM) prior to approximate the distribution of random errors within the general semiparametric random effects models with parametric random effects for longitudinal data setting and failure time setting separately. In a survival setting with clusters, we propose a new class of nonparametric random effects models which is motivated from the accelerated failure models. We employ a beta process prior to tact clustering and estimation simultaneously. We analyze a new data set integrated from Alzheimer's disease (AD) study to illustrate the presented model and methods.

Keywords: beta process; Dirichlet process mixture; clustered data; longitudinal data; random effects; survival outcome; nonparametric transformation model (search for similar items in EconPapers)
JEL-codes: C60 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:201118

DOI: 10.5772/intechopen.88822

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