Posterior consisteny of logistic random effect models
Seminar Room 1, Newton Institute
We study the posterior consistency of the logistic random effect models. Usual parametric priors are put on the regression coefficients of the fixed effects and a nooparametric prior such as Dirichlet process or Polya tree is put on the distribution of random effects. We give sufficient conditions for the consistency of the joint posterior of the regression coefficients and random effect distribution, and explain how to prove it. Also, we discuss the limitation of the proposed sufficient conditions and possible extensions.