We present a Bayesian change point multiple regression methodology which simultaneously estimates the location of change points/regimes, the corresponding subset of independent variables per regime, as well as the associated regimes' regression parameters. Unlike existing switching multiple...

We review mixture models that relate a dependent variable to a set of exogenous or explanatory variables. Also, we describe a generalized linear regression mixture model that encompasses previously developed models as special cases. The model allows for a probabilistic classification of...

This paper presents a conditional mixture, maximum likelihood methodology for performing clusterwise linear regression. This new methodology simultaneously estimates separate regression functions and membership in K clusters or groups. A review of related procedures is discussed with an...

Ambiguity surrounds any importance measure in cases in which predictor variables are correlated. However, a new measure is proposed that has attractive properties, such as providing individual contributions that are both non-negative and sum to R2. The new measure is compared with four other...