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Analysis of the posterior for spline estimators in logistic regression

✍ Scribed by Nandini Raghavan; Dennis D. Cox

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
172 KB
Volume
71
Category
Article
ISSN
0378-3758

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✦ Synopsis

A 'partially improper' Gaussian prior is considered for Bayesian inference in logistic regression. This includes generalized smoothing spline priors that are used for nonparametric inference about the logit, and also priors that correspond to generalized linear mixed models. Necessary and su cient conditions are given for the posterior to be a proper probability measure, and bounds are given for the tails of the posterior density. These results are applied to investigate Monte Carlo methods for approximating the posterior.

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