This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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148
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Type:
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Invited
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Date/Time:
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Monday, August 2, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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| Abstract - #306059 |
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Title:
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Sufficient Dimension Reduction and Variable Selection for Censored Regression
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Author(s):
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Wenbin Lu*+ and Lexin Li
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Companies:
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North Carolina State University and North Carolina State University
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Address:
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5212 SAS Hall, 2311 Stinson Drive, Raleigh, NC, 27695,
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Keywords:
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Central subspace ;
ICPW estimation ;
Sliced inverse regression ;
Sufficient dimension reduction ;
Variable selection
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Abstract:
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Methodology of sufficient dimension reduction (SDR) has offered an effective means to facilitate regression analysis of high dimensional data. When the response is censored, however, most existing SDR estimators can not be applied, or require some restrictive conditions. In this work we propose a new class of inverse censoring probability weighted SDR estimators for censored regression. Moreover, regularization is introduced to achieve simultaneous variable selection and dimension reduction. Asymptotic properties and empirical performance of the proposed methods are examined.
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