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

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  • Chung, Hwan
  • Chang, Hsiu-Ching

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.

Suggested Citation

  • Chung, Hwan & Chang, Hsiu-Ching, 2012. "Bayesian approaches to the model selection problem in the analysis of latent stage-sequential process," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4097-4110.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:12:p:4097-4110
    DOI: 10.1016/j.csda.2012.03.015
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    References listed on IDEAS

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    1. Chung, Yeonseung & Dunson, David B., 2009. "Nonparametric Bayes Conditional Distribution Modeling With Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1646-1660.
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    4. Hwan Chung & James C. Anthony & Joseph L. Schafer, 2011. "Latent class profile analysis: an application to stage sequential processes in early onset drinking behaviours," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(3), pages 689-712, July.
    5. David B. Dunson & Ju-Hyun Park, 2008. "Kernel stick-breaking processes," Biometrika, Biometrika Trust, vol. 95(2), pages 307-323.
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