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Abstract Details
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Activity Number:
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593
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Type:
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Invited
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #300156 |
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Title:
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Groupwise Dimension Reduction
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Author(s):
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Lexin Li*+ and Bing Li and Lixing Zhu
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Companies:
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North Carolina State University and Penn State University and Hong Kong Baptist University
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Address:
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Department of Statistics, Raleigh, NC, 27695, USA
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Keywords:
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Sufficient dimension reduction ;
Direct sum of differential operators ;
Minimum average variance estimation ;
Partial dimension reduction
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Abstract:
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In many regression applications, the predictors fall naturally into a number of groups or domains, and it is often desirable to establish a domain-specific relation between the predictors and the response. In this talk, we consider dimension reduction that incorporates such domain knowledge. Our formulation also accommodates the situations where dimension reduction is focused only on part of the predictors. Through simulation and real data analyses, we show that the proposed method achieves greater accuracy and interpretability than the dimension reduction methods that ignore group information.
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Authors who are presenting talks have a * after their name.
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