Abstract: This article considers hypothesis testing using Bayes factor in the context of categorical data models represented in two dimensional contingency tables. The study includes multinomial model for a general I × J table data. Other data characteristics such as low as well as polarized cell counts and size of the tables are also considered. The objective is to investigate the sensitivity of Bayes factor taking these features into account so as to understand the performance of non-informative priors itself. Consistency has been studied based on different types of data and using Dirichlet prior with eight different choices for multinomial model followed by a bootstrap simulation. Study has emphasized the reasonable choice of values for the parameters that normally represents the underlying physical phenomena, though partially vague in nature.
Abstract: In this paper we introduce bivariate Weibull distributions derived from copula functions in presence of cure fraction, censored data and covariates. Two copla functions are explored: the FGM (Farlie - Gumbel Morgenstern) copula and the Gumbel copula. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a medical data set.