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Abstract Details
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Activity Number:
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28
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #303952 |
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Title:
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Sparse Group Sufficient Dimension Reduction
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Author(s):
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Xuerong Wen*+ and Bilin Zeng and Lixing Zhu
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Companies:
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Missouri University of Science and Technology and Missouri University of Science and Technology and Hong Kong Baptist University
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Address:
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1870 Miner Circle, Rolla, MO, 65409, United States
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Keywords:
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sparse group lasso ;
sufficient dimension reduciton ;
gene pathway data ;
generalized additive modeldel
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Abstract:
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We adopt the idea of sparse group lasso (Friedman et al., 2010) to the framework of the sufficient dimension reduction (Li 1991, Cook 1998). A distribution transformation based sparse group lasso is introduced to conduct group and variable selections simultaneously. The consistency of our selection method is studied. Our method does not require a linear relationship between the response and predictors, and is robust to outliers. One immediate application of our method is to the gene pathway data analysis. Simulation studies and data analysis are included for illustration of our method.
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