Bayesian quantile regression for longitudinal data models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

30 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1635-1649
Journal / PublicationJournal of Statistical Computation and Simulation
Volume82
Issue number11
Online published5 Jul 2011
Publication statusPublished - 2012
Externally publishedYes

Abstract

In this paper, we discuss a fully Bayesian quantile inference using Markov Chain Monte Carlo (MCMC) method for longitudinal data models with random effects. Under the assumption of error term subject to asymmetric Laplace distribution, we establish a hierarchical Bayesian model and obtain the posterior distribution of unknown parameters at τ-th level. We overcome the current computational limitations using two approaches. One is the general MCMC technique with Metropolis-Hastings algorithm and another is the Gibbs sampling from the full conditional distribution. These two methods outperform the traditional frequentist methods under a wide array of simulated data models and are flexible enough to easily accommodate changes in the number of random effects and in their assumed distribution. We apply the Gibbs sampling method to analyse a mouse growth data and some different conclusions from those in the literatures are obtained.

Research Area(s)

  • asymmetric Laplace distribution, Bayesian inference, Gibbs sampler, longitudinal data, Markov Chain Monte Carlo, Metropolis-Hastings algorithm, quantile regression