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Abstract Details
Activity Number:
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346
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract - #303388 |
Title:
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Semiparametric Function Estimation Using Shrinkage Techniques
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Author(s):
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Mohamed Amezziane*+ and Syed Ejaz Ahmed
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Companies:
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DePaul University and University of Windsor
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Address:
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, , 60660,
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Keywords:
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pre-test estimators ;
nonparametric estimation ;
shrinakge estimation ;
smoothing parameter ;
shrinkage coefficient
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Abstract:
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We use shrinkage techniques to develop a class of semiparametric estimators of functions (distribution, density, regression, etc.) which can be obtained as linear combinations of fully determined parametric functions and nonparametric function estimators. We present the asymptotic properties of the proposed class of estimators and compare their performance to that of classical nonparametric estimators. Moreover, we show that the proposed estimators do not require the use of optimally selected smoothing parameters and are therefore less sensitive to the effect of curse of dimensionality.
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