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Abstract Details
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Activity Number:
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189
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section for Statistical Programmers and Analysts
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| Abstract - #302257 |
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Title:
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Shrinkage and Absolute Penalty Estimation in Linear Models
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Author(s):
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S.M. Enayetur Raheem*+ and Ejaz Syed Ahmed
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Companies:
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University of Windsor and University of Windsor
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Address:
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401 Sunset Ave, Windsor, ON, N9B3P4, Canada
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Keywords:
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Shrinkage estimation ;
Absolute Penalty Estimation ;
Linear Regression ;
James-Stein Estimator
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Abstract:
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We study the James-Stein-type shrinkage estimation of regression parameters in a linear regression setup. Shrinking of the parameters towards both the null vector and a sub-vector is considered. We conduct Monte Carlo simulation to compare, under quadratic risk criterion, an absolute penalty estimator and James-Stein-type shrinkage estimator. Analytic results show that shrinkage estimators demonstrate asymptotically superior risk performance relative to the classical estimator. Our Monte Carlo study reveals the superiority of shrinkage estimators over absolute penalty estimators in the presence of a relatively large number of nuisance covariates. Application to a real life data set will be provided.
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