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Abstract Details
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Activity Number:
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448
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #306001 |
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Title:
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Clustering Analysis for Functional Data
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Author(s):
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Chae Young Lim*+ and Sarat C Dass and Tapabrata Maiti
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Companies:
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Michigan State University and Michigan State University and Michigan State University
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Address:
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Department of Statistics and Probability, East Lansing, MI, , United States
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Keywords:
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cluster analysis ;
functional data ;
Bayesian method
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Abstract:
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There are several approaches to cluster functional data. A well known approach is to smooth functional observations first and then apply a clustering algorithm to the estimated coefficents for smooth functions. We propose a Bayesian approach to smooth functional observations using splines and cluster them concurrently. Our approach is adaptive in that the number of knots and locations of knots are estimated as well.
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Authors who are presenting talks have a * after their name.
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