Semiparametric finite mixture model estimation algorithms

Statistics and Modeling for Complex Data

We present ideas for estimation algorithms in finite mixture models where components are not assumed to come from a particular parametric family. The algorithms, which combine elements of standard EM algorithms and kernel density estimation, are iterative and may be shown to be monotonic in the sense that they guarantee an increase in a smoothed loglikelihood objective function at each iteration. We consider applications of these ideas to multivariate psychological measurements and mixtures of regressions.