Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/23865
Authors: 
Holmes, C
Knorr-Held, L
Year of Publication: 
2003
Series/Report no.: 
Discussion papers / Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 306
Abstract: 
In this paper we highlight a data augmentation approach to inference in the Bayesian logistic regression model. We demonstrate that the resulting conditional likelihood of the regression coefficients is multivariate normal, equivalent to a standard Bayesian linear regression, which allows for efficient simulation using a block Gibbs sampler. We illustrate that the method is particularly suited to problems in covariate set uncertainty and random effects models
Subjects: 
Auxiliary variables
Bayesian logistic regression
Data augmentation
Markov chain Monte Carlo
Model averaging
Random effects
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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