An MCMC model search algorithm for regression problems

In this paper, we improve upon the Carlin and Chib Markov chain Monte Carlo algorithm that searches in model and parameter spaces. Our proposed algorithm attempts non-uniformly chosen ‘local’ moves in the model space and avoids some pitfalls of other existing algorithms. In a series of examples with linear and logistic regression, we report evidence that our proposed algorithm performs better than the existing algorithms.     

Sample Size Determination for Comparing Two Poisson Rates with Underreported Counts

The optimal sample size comparing two Poisson rates when the counts are underreported is investigated. We consider two sampling scenarios. We first consider the case where only underreported data will be sampled and rely on informative prior distributions to obtain posterior identifiability. We also consider the case where an expensive infallible search method and a fallible method are available. An interval based sample size criterion is used in both sampling scenarios. Since the posterior distributions of the two rates are functions of confluent hypergeometric and hypergeometric functions simulation based methods are necessary to perform the sample size determination scheme.