Nonparametric estimation in semi-parametric univariate mixture models
We introduce new estimates of the mixing proportions, locations, and variances of the components of a finite univariate mixture model. We assume that the components are symmetric and differ only in the locations. No parametric model assumptions are imposed on the components. Further, when there is additional information available in the form of training samples that contain information concerning the mixing proportion, the new methods are robust to the symmetry assumption.
training samples em algorithm finite mixture models