Abstract #302060

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JSM 2003 Abstract #302060
Activity Number: 95
Type: Contributed
Date/Time: Monday, August 4, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #302060
Title: Selecting the Number of Components in a Mixture Model
Author(s): Surajit Ray*+
Companies: Pennsylvania State University
Address: 0326 Thomas Building, University Park, PA, 16802-2111,
Keywords: mixture of normals ; quadratic distance ; multivariate bimodality ; kernel-based distance ; gene expression ; risk analysis
Abstract:

Multivariate mixture models provide well known and widely used methods for density estimation, model-based clustering, and explanations for the data generation process. However, the problem of choosing the number of components of a mixture model in a statistically meaningful way is still a subject of considerable research. I introduce several rules for selecting a finite mixture model, and hence estimating the number of components, using quadratic distance functions. In one approach, the goal is to find the minimal number of components that are needed to adequately describe the true distribution, where the decision is based on a nonparametric confidence set for the true distribution. Two alternative approaches for estimating the number of components are based on density concordance and risk analysis. Applications of my methods to gene expression data will be presented during the talk.

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