A di use prior limit in semiparametric binary regressionMichael

We place a standard Dirichlet process prior on the inverse-link function of a binary regression model and study the posterior distribution as the prior precision parameter converges to zero. Simple closed-form expressions are available for the limiting posterior density of the regression parameters and the posterior predictive distribution. This limiting posterior exhibits instability as the sample size grows and does not generally produce consistent estima-tors. A more stable variation of the Dirichlet process prior is also discussed.