JSM 2005 - Toronto

Abstract #302857

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 110
Type: Other
Date/Time: Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor: ASA
Abstract - #302857
Title: Model-based Clustering Probability Density Estimation
Author(s): Adrian E. Raftery*+
Companies: University of Washington
Address: Department of Statistics, Seattle, WA, 98195-4320,
Keywords: finite mixtures ; clustering ; EM algorithm ; Bayes factor
Abstract:

The need to estimate multivariate probability densities arises in many applications such as discriminant analysis, clustering, nonparametric regression and smoothing, image processing, and gene expression analysis. This introductory overview session consists of two lectures in the areas of nonparametric and semiparametric probability density estimation. The second talk will illustrate a Gaussian finite-mixture-based approach to probability density estimation called model-based clustering. Dr. Raftery is one of the original developers of the model-based clustering approach. This is a powerful framework that can be applied to basic problems in multivariate statistics, such as discriminant analysis, clustering, and multivariate density estimation.

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Revised March 2005