Bayesian estimation of unrestricted and order-restricted association models for a two-way contingency table

Abstract

In two-way contingency tables analysis, a popular class of models for describing the structure of the association between the two categorical variables are the so-called “association” models. Such models assign scores to the classification variables which can be either fixed and prespecified or unknown parameters to be estimated. Under the row–column (RC) association model, both row and column scores are unknown parameters without any restriction concerning their ordinality. It is natural to impose order restrictions on the scores when the classification variables are ordinal. The Bayesian approach for the RC (unrestricted and restricted) model is adopted. MCMC methods are facilitated in order the parameters to be estimated. Furthermore, an alternative parametrization of the association models is proposed. This new parametrization simplifies computation in the MCMC procedure and leads to a natural parameter space for the order constrained model. The proposed methodology is illustrated via a popular dataset.