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Activity Number:
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166
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Type:
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Invited
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Date/Time:
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Monday, August 4, 2008 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300139 |
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Title:
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Sufficient Dimension Reduction for Small N, Large P Regressions
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Author(s):
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Lexin Li*+ and R. Dennis Cook and Chih-Ling Tsai
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Companies:
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North Carolina State University and The University of Minnesota and University of California, Davis
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Address:
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Department of Statistics, Raleigh, NC, 27695,
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Keywords:
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Partial least squares ; Single-index model ; Sliced inverse regression
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Abstract:
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In regressions with a vector of quantitative predictors, sufficient dimension reduction methods can effectively reduce the predictor dimension, while preserving full regression information and assuming no parametric model. However, current reduction methods require the sample size n to be greater than the number of predictors p. It is well known that partial least squares can deal with problems with n < p. In this talk, we first establish a link between partial least squares and sufficient dimension reduction framework. Motivated by this link, we then propose a new dimension reduction method that works for n < p regressions. Both simulations and real data analysis will be presented to demonstrate effectiveness of the proposed method.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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