Finite mixtures of parametric distributions are often used to model data of which it is known or suspected that there are subpopulations. Instead of a parametric model, a penalized likelihood smoothing algorithm is developed. The penalty is chosen to favor a log-concave result. The standard EM algorithm (“split and fit”) can be used. Theoretical results and applications are presented. © 2006 Elsevier B.V. All rights reserved.
Paul H. C. Eilers, M. W. Borgdorff