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Variable selection for multivariate logistic regression models. (English) Zbl 1027.62015

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.
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:

62F15 Bayesian inference
65C60 Computational problems in statistics (MSC2010)
62J12 Generalized linear models (logistic models)
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