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Activity Number:
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488
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Type:
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Invited
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Date/Time:
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Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #305105 |
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Title:
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Functional Clustering by Bayesian Wavelet Methods
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Author(s):
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Bani K. Mallick*+
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Companies:
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Texas A&M University
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Address:
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Department of Statistics, 3143 TAMU, College Station, TX, 77845,
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Keywords:
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functional clustering ; wavelets ; mixture of Dirichlet ; microarray gene expression data
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Abstract:
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We propose a nonparametric Bayes wavelet model for clustering of functional data. The wavelet-based methodology is aimed at the resolution of generic global and local features during clustering and is suitable for clustering high-dimensional data. Based on the Dirichlet process (DP), the nonparametric Bayes model extends the scope of traditional Bayes wavelet methods to functional clustering and allows the elicitation of prior belief about the regularity of the functions and the number of clusters by suitably mixing the DP. Posterior inference is carried out by Gibbs sampling with conjugate priors, which makes the computation straightforward. The models have been used successfully to analyze time course microarray gene expression profiles.
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- Authors who are presenting talks have a * after their name.
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