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Activity Number: 283
Type: Contributed
Date/Time: Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #309440
Title: Variable Selection in Penalized Model-Based Clustering with Cluster-Dependent Diagonal Covariances with Application to Microarray Data
Author(s): Benhuai Xie*+ and Wei Pan and Xiaotong Shen
Companies: The University of Minnesota and The University of Minnesota and The University of Minnesota
Address: A460 Mayo Building MMC 303, Minneapolis, MN, 55455,
Keywords: Penalization ; Mixture model ; EM algorithm ; BIC
Abstract:

Recently Pan and Shen proposed a penalized model-based method to realize variable selection in cluster analysis for high-dimensional data. However, their method requires a common diagonal covariance matrix across clusters. Here we consider a more general situation with cluster-dependent diagonal covariance matrices, for which both mean and variance parameters have to be penalized to realize variable selection. An EM algorithm is derived to obtain the maximum penalized likelihood estimates. Numerical examples are provided to demonstrate the utility of the proposed method.

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Revised September, 2007