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Abstract Details
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Activity Number:
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303
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #300541 |
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Title:
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On Partial Sufficient Dimension Reduction
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Author(s):
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Xuerong Wen*+ and Lixing Zhu and Becky Feng
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Companies:
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Missouri University of Science and Technology and Hong Kong Baptist University and Hong Kong Baptist University
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Address:
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400 W. 12th St. , Rolla, MO, 65409, USA
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Keywords:
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sufficient dimension reduction ;
partial central subspace ;
partially linear single-index model
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Abstract:
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Under the general framework of partial sufficient dimension reduction (Chiaromonte et al. 2002), we generalize the notion of partial central subspace from categorical W to continuous ones. Asymptotic properties and small sample properties are also studied. Our method provides a general solution to partial dimension reduction when it is more desirable to conduct dimension reduction on part of the predictors (X) while incorporating the prior information from W (whether W is categorical or continuous), rather than to treat all components of the predictors (X,W) indiscriminately. One immediate application is to the well-known partially linear single-index or multiple-index model (Wang et al. 2010, Carrol et al. 1997).
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