Bayesian approaches to the model selection problem in the analysis of latent stage-sequential process

  • Authors:

  • Hwan Chung;Hsiu-Ching Chang

  • Affiliations:

  • Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 136-701, Republic of Korea;Department of Clinical Epidemiology and Biostatistics, Blue Cross Blue Shield of Michigan, 500 Rencen, Detroit, MI 48226, USA

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2012

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Abstract

Recently, a great deal of attention has been paid to the stage-sequential process for the longitudinal data. A number of methods for analyzing stage-sequential processes have been derived from the family of finite mixture modeling. However, the research on the sequential process is rendered difficult by the fact that the number of latent components is not known a priori. To address this problem, we adopt the reversible jump MCMC (RJMCMC) and the Bayesian nonparametric approach, which provide a set of principles for the systematic model selection for the stage-sequential process. Using a latent class profile analysis, we evaluate the performance of RJMCMC and the Bayesian nonparametric method on the model selection problem.