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Abstract Details
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Activity Number:
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635
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Type:
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Invited
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300136 |
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Title:
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Estimating Sufficient Reductions of the Predictors in Abundant High-Dimensional Regressions
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Author(s):
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R. Dennis Cook*+ and Liliana Forzani and Adam Rothman
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Companies:
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University of Minnesota and Universidad Nacional del Litoral Instituto Matematica Aplicada Litoral - CONICET and University of Minnesota
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Address:
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School of Statistics, Minneapolis, MN, 55455,
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Keywords:
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Sufficient dimension reduction ;
principal fitted componens ;
central subspace
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Abstract:
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Allowing the sample size and the number of predictors to diverge in almost any relationship, we will discuss the asymptotic behavior of a class of methods for estimating sufficient reductions of the predictors in high dimensional regressions. Emphasis will be given to prediction rather than variable selection and to abundant regressions where the ratio of active to inactive predictors is bounded away from zero. Some estimators in the class to be described possess a useful oracle property. Simulation and a prediction problem from spectroscopy will be used to illustrate the results.
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