|
Activity Number:
|
480
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 7, 2008 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Statistical Computing
|
| Abstract - #300560 |
|
Title:
|
On a Mixture of Skew T Distributions
|
|
Author(s):
|
Wan Ju Hsieh*+ and Tsung-I Lin
|
|
Companies:
|
National Chiao Tung University and National Chung Hsing University
|
|
Address:
|
Institute of Statistics, Hsinchu, 300, Taiwan
|
|
Keywords:
|
EM-type algorithms ; maximum likelihood ; outlying observations ; PX-EM algorithm ; skew t mixtures ; truncated normal
|
|
Abstract:
|
A finite mixture model using the Student's t distribution has been recognized as a robust extension of normal mixtures. Recently, a mixture of skew normal distributions has been found to be effective in the treatment of heterogeneous data involving asymmetric behaviors across subclasses. In this article, we propose a robust mixture framework based on the skew t distribution to efficiently deal with heavy-tailedness, extra skewness and multimodality in a wide range of settings. Statistical mixture modeling based on normal, Student's t and skew normal distributions can be viewed as special cases of the skew t mixture model. We present analytically simple EM-type algorithms for iteratively computing maximum likelihood estimates. The proposed methodology is illustrated by analyzing a real data example.
|
