Bayesian model selection for constrained multivariate normal linear models

The expectations that researchers have about the structure in the data can often be formulated in terms of equality constraints and/or inequality constraints on the parameters in the model that is used. In a (M)AN(C)OVA model, researchers have expectations about the differences between the (adjusted) group means; in a repeated measures model, expectations can be stated between the measuremenet means over time; and in a (multivariate) regression model, expectations can be stated between the (standardized) regression coefficients. Based on different theories, different expectations can be formulated into a set of competing equality and inequality constrained models. The researcher is then interested which model receives most support from the data. This dissertation explores how the Bayes factor, a Bayesian model selection, can be used for this purpose.