Martial Longla
University of Mississippi
USA
Title: An objective Bayesian estimation of parameters in a log-binomial model
Biography
Biography: Martial Longla
Abstract
The log-binomial model is commonly recommended for modeling prevalence ratio just as logistic regression is used to model log odds-ratio. However, for the log-binomial model, the parameter space turns out to be restricted causing difficulties for the maximum likelihood estimation in terms of convergence of numerical algorithms and calculation of standard errors. The Bayesian approach is a natural choice for modeling log-binomial model, as it involves neither maximization nor large sample approximation. We consider two objective or non-informative priors for the parameters in a log-binomial model: an improper prior, and a proper prior. We give sufficient conditions for the posterior from the improper at prior to be proper, and compare the two priors in terms of the resulting posterior summaries. We use Markov Chain Monte Carlo via slice sampling to simulate from the posterior distributions. An overview of recent contributions to this problem will be provided. We will also present questions involving dependence.