Bayesian Inequality Constrained Models for Categorical Data

Abstract

In recent years, there has been growing interest in statistical models incorporating inequality constraints on model parameters. This is because the omnibus hypotheses can be replaced by more specific inequality constrained hypotheses. In this thesis several models that deal with categorical data are extended to be able to handle inequality constraints on the model parameters.