Order Selection in Finite Mixture Models With a Nonsmooth Penalty
Order Selection in Finite Mixture Models With a Nonsmooth Penalty
Order selection is a fundamental and challenging problem in the application of finite mixture models. We develop a new penalized likelihood approach that we call MSCAD. MSCAD deviates from information-based methods, such as Akaike information criterion and the Bayes information criterion, by introducing two penalty functions that depend on the …