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Activity Number: 69
Type: Contributed
Date/Time: Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Computing
Abstract - #305427
Title: On the Mixture of Multivariate Skew Normal Distributions
Author(s): Jack C. Lee*+ and Tsung-I Lin
Companies: National Chiao Tung University and National Chung Hsing University
Address: Institute of Statistics, Hsinchu, 300, Taiwan
Keywords: EM algorithm ; Fisher information ; normal mixture model ; skew normal mixtures ; stochastic representation ; truncated normal distributions
Abstract:

A finite mixture of distributions, particularly the use of normal components, has received much attention and is known to be powerful for modeling an extremely wide variety of random phenomena. However, the usefulness of normal mixture models is somewhat limited, and there still exists drawbacks in various applied problems. In this paper, we introduce a flexible mixture modeling framework using the multivariate skew normal distribution. A feasible EM algorithm is developed for carrying out maximum likelihood estimation of parameters. In addition, a general information-based method for obtaining the asymptotic covariance matrix of maximum likelihood estimates is presented. We apply the procedures to a real multivariate dataset and compare the results with those from fitting Gaussian mixtures.

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