Wednesday, October 28, 2009
03:30 PM in REC 315
Professor Michael Levine
Department of Statistics, Purdue University

Mixing Density and Mixture Density Estimation: Estimation Methods and Possible Connections

Abstract

It is well known that when the true mixing distribution is continuous, its nonparametric maximum likelihood is degenerate. I will discuss an alternative method that maximizes a penalized likelihood instead. The resulting estimate is called the nonparametric maximum penalized likelihood estimate (NPMPLE). A functional EM algorithm is proposed for computing the NPMPLE of the continuous mixing density. This is a joint work with Michael Y. Zhu.

Time permitting, I will also discuss possible connections between mixing density estimation and the estimation of finite mixtures of nonparametric components. This will be a discussion of the current joint work with David Hunter and Didier Chauveaux.