This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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284
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #308617 |
Title:
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Model-Based Semisupervised Clustering
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Author(s):
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Volodymyr Melnykov and Wei-Chen Chen* and Ranjan Maitra+
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Companies:
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North Dakota State University and Iowa State University and Iowa State University
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Address:
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Department of Statistics, Ames, IA, 50011-1210,
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Keywords:
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semi-supervised clustering ;
mixture model ;
EM algorithm ;
gene expression ;
microarray data
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Abstract:
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Semi-supervised clustering groups observations in a scenario where only some group identifies are available. We provide a model-based approach to this problem. Model parameters are estimated by the expectation-maximization (EM) algorithm, for which initialization strategies are also developed. A rigorous significance-based approach to estimating number of components is established and has better performance than other information criteria. Simulation experiments in a wide range of settings show improvements in predictions of number of components and classification. The method is applied to finding co-regulated expressed genes in a microarray gene expression study.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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