Estimating Mixtures of Regressions

Abstract

AbstractThis article shows how Bayesian inference for switching regression models and their generalizations 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 birthand-death technique developed in recent literature. The methods are illustrated on various real datasets.Key Words: Bayesian inferenceBirth-and-death processLabel switchingLogistic regressionLoss functionsMCMC algorithmsPoisson regressionSwitching regression