Journal
CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
Volume 30, Issue 1, Pages 55-78Publisher
CANADIAN JOURNAL STATISTICS
DOI: 10.2307/3315865
Keywords
Bayesian criterion; data augmentation; Gibbs sampling; historical data; logistic regression; Poisson regression; predictive distribution; prior distribution
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The authors propose methods for Bayesian inference for generalized linear models with missing covariate data. They specify a parametric distribution for the covariates that is written as a sequence of one-dimensional conditional distributions. They propose an informative class of joint prior distributions for the regression coefficients and the parameters arising from the covariate distributions. They examine the properties of the proposed prior and resulting posterior distributions. They also present a Bayesian criterion for comparing various models, and a calibration is derived for it. A detailed simulation is conducted and two real data sets are examined to demonstrate the methodology.
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