The minimum density power divergence (MDPD) framework (Basu et al., 1998) provides a family of estimators indexed by a parameter (α), which controls the tradeoff between efficiency and robustness. In this paper, we extend this estimation framework to finite mixtures of regression models. In...

Finite mixture models provide a natural way of modeling continuous or discrete outcomes that are observed from populations consisting of a finite number of homogeneous subpopulations. Applications of finite mixture models are abundant in the social and behavioral sciences, biological and...