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Zbl 1027.62015
Chen, Ming-Hui; Dey, Dipak K.
Variable selection for multivariate logistic regression models. (English)
[J] J. Stat. Plann. Inference 111, No.1-2, 37-55 (2003). ISSN 0378-3758

Summary: We use multivariate logistic regression models to incorporate correlation among binary response data. Our objective is to develop a variable subset selection procedure to identify important covariates in predicting correlated binary responses using a Bayesian approach. In order to incorporate available prior information, we propose a class of informative prior distributions on the model parameters and on the model space. \par The propriety of the proposed informative prior is investigated in detail. Novel computational algorithms are also developed for sampling from the posterior distribution as well as for computing posterior model probabilities. Finally, a simulated data example and a real data example from a prostate cancer study are used to illustrate the proposed methodology.

MSC 2000:: *62F15 Bayesian inference
65C60 Computational problems in statistics
62J12 Generalized linear models

Keywords: Bayesian computation; Hierarchical model; Markov chain Monte Carlo; Prior elicitation; Propriety

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Zentralblatt MATH Berlin [Germany]