Bayesian Analysis for Random Effects Binomial Regression
Dal-Ho Kim, Eunyoung Kim · Communications for Statistical Applications and Methods · 2000
In this paper, we investigate the Bayesian approach to random effect binomial regression models with improper prior due to the absence of information on parameter. We also propose a method of estimating the posterior moments and prediction and discuss some general methods for studying model assessment. The methodology is illustrated with Crowder's Seeds Data. Markov Chain Monte Carlo techniques are used to overcome the computational difficulties.