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Abstract Details
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Activity Number:
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75
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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| Abstract - #306511 |
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Title:
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Sufficient Dimension Folding for Regression Mean Function
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Author(s):
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Yuan Xue*+ and Xiangrong Yin
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Companies:
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University of Georgia and University of Georgia
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Address:
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84 Venita Dr, Athens, GA, , United States
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Keywords:
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Central Folding Subspace ;
Central Mean Folding Subspace ;
Minimum Average Variance Estimation
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Abstract:
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In this paper, we consider the sufficient dimension folding for the regression mean function of matrix objects. We introduce the central mean folding subspace and propose two local estimation methods: folded outer product of gradients estimation (folded-OPG) and folded minimum average variance estimation (folded-MAVE). We evaluate their performance via simulated models. A modified BIC criterion is used to determine the dimension of the left or right central mean folding subspace. We apply our estimation methods to part of the primary biliary cirrhosis data follow up to a Mayo Clinic trail.
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