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Abstract Details
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Activity Number:
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527
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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SSC
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| Abstract - #306658 |
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Title:
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Stein Rules in Some Heteroscedastic Linear Model
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Author(s):
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Severien Nkurunziza*+
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Companies:
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University of Windsor
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Address:
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401 Sunset Avenue, Windsor, ON, N9B 3P4, Canada
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Keywords:
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Asymptotic distributional risk ;
Heteroscedastic linear model ;
Shrinkage estimators ;
Stein rules ;
LSE
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Abstract:
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In this paper, we study inference problem concerning the regression parameter vector of some heteroscedastic linear model. More specifically, we are interested in the scenario where the parameter vector is suspected to lie in some linear subspace. Given such imprecise prior information, we develop shrinkage estimators which dominates improve over the performance of the classical least squares estimator. Under an asymptotic distributional quadratic risk criterion, we establish the relative dominance of the established estimators. Further, we carry out some simulation studies for observation periods of small and moderate lengths of time that illustrate the performance of the proposed method.
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Authors who are presenting talks have a * after their name.
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