Estimating Mixtures of Regressions

In this paper, we show how Bayesian inference for switching regression models and their generalisations can be achieved by the specification of loss functions which overcome the label switching problem common to all mixture models. We also derive an extension to models where the number of components in the mixture is unknown, based on the birth-and-death technique developed in Stephens (2000). The methods are illustrated on various real datasets.