Thursday, November 12, 2009
04:30 PM in MATH 175
David Hunter
Department of Statistics, Pennsylvania State University

Estimation for Nonparametric Mixture Models

Abstract

We present an algorithm for estimation in finite mixture models where the observations are multivariate with conditionally independent coordinates but their distributions are otherwise completely unspecified. This algorithm is an extension and modification of the recent EM-like algorithm of Bordes, Chauveau, and Vandekerkhove (2007) for univariate mixture models with symmetric components, which we will also discuss. Unlike in the univariate case, the multivariate algorithm does not necessarily require an assumption on the component density functions for the model parameters to be identifiable. We explain what is known about identifiability, show why our algorithm is more appealing than other algorithms for this problem, and discuss some remaining open questions.

Refreshments will be served at 4:00 PM in HAAS 111.