This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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662
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract - #307989 |
Title:
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Model Selection of Correlation Structure for Clustered Data
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Author(s):
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Jianhui Zhou*+ and Peiyong (Annie) Qu
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Companies:
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University of Virginia and University of Illinois at Urbana-Champaign
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Address:
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Department of Statistics, Charlottesville, VA, 22904, U.S.
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Keywords:
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Bayesian information criterion ;
clustered data ;
correlation structure ;
oracle property ;
quadratic inference function
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Abstract:
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Model selection of correlation structure is challenging because it involves higher order of moments than covariate selection, and the high dimension of correlation parameters could make estimation unreliable. In this paper, we select the correlation structure for clustered data from candidate structures through a group-wise basis matrices selection strategy. The proposed method has the advantages of not requiring the likelihood function and of being computationally efficient. Also, the method can identify complex correlation structures. Furthermore, it is not restricted by large cluster size, and is applicable for both continuous and discrete responses. In theory, the proposed method enjoys the oracle property of selecting the true correlation structure consistently and estimating the correlation parameters with the same asymptotic normal distribution as if the true structure is known.
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