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Bayesian variable selection for logistic mixed model with nonparametric random effects

  • Yang, Mingan
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    In analyzing correlated data or clustered data with linear or logistic mixed effects model, one commonly assumes that the random effects follow a normal distribution with mean zero. However, this assumption might not be appropriate in many cases. In particular, substantial violation of normality assumption might potentially impact the subset selection of variables in these models. In this article, we address the problem of joint selection of both fixed and random effects and bias control for random effects in nonparametric settings. An efficient Bayesian variable selection is implemented using a stochastic search Gibbs sampler to allow both fixed and random effects to be dropped effectively out of the model. The approach is illustrated using a simulation study and a real data example.

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    File URL: http://www.sciencedirect.com/science/article/pii/S016794731100449X
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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 9 ()
    Pages: 2663-2674

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:9:p:2663-2674
    DOI: 10.1016/j.csda.2011.12.014
    Contact details of provider: Web page: http://www.elsevier.com/locate/csda

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    1. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
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    6. Smith, Michael & Kohn, Robert, 1996. "Nonparametric regression using Bayesian variable selection," Journal of Econometrics, Elsevier, vol. 75(2), pages 317-343, December.
    7. 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.
    8. Omiros Papaspiliopoulos & Gareth O. Roberts, 2008. "Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models," Biometrika, Biometrika Trust, vol. 95(1), pages 169-186.
    9. Satkartar K. Kinney & David B. Dunson, 2007. "Fixed and Random Effects Selection in Linear and Logistic Models," Biometrics, The International Biometric Society, vol. 63(3), pages 690-698, 09.
    10. Mingan Yang & David Dunson, 2010. "Bayesian Semiparametric Structural Equation Models with Latent Variables," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 675-693, December.
    11. Yang, Mingan & Dunson, David B. & Baird, Donna, 2010. "Semiparametric Bayes hierarchical models with mean and variance constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2172-2186, September.
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