title: EM Estimation for Finite Mixture Models with Known Mixture Component Size.
abstract: We consider the use of an EM algorithm for fitting finite mixture models when mixture component size is known. This situation can occur in a number of settings, where individual membership is unknown but aggregate membership is known. When the mixture component size, i.e., the aggregate mixture component membership, is known, it is common practice to treat only the mixing probability as known. This approach does not, however, entirely account for the fact that the number of observations within each mixture component is known, which may result in artificially incorrect estimates of parameters. By fully capitalizing on the available information, the proposed EM algorithm shows robustness to the choice of starting values and exhibits numerically stable convergence properties.
authors: Applied Statistics Group, E. I. du Pont de Nemours & Company, DE, USA.
authors: Chen
authors: C
authors: Teel
authors: Department of Applied Statistics, Yonsei University, Seoul, Korea.
authors: Taeyoung
authors: T
authors: Park
authors: Department of Statistics, University of Pittsburgh, PA, USA.
authors: Allan R
authors: AR
authors: Sampson
keywords: Aggregate data; Conditional Bernoulli distribution; EM algorithm; Finite mixture models