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Activity Number: 130
Type: Contributed
Date/Time: Monday, August 3, 2009 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract - #305627
Title: Variable Selection, Constrained and Shrinkage Estimation in Multivariate Regression Models
Author(s): Ejaz Syed Ahmed*+ and Severien Nkurunziza
Companies: University of Windsor and University of Windsor
Address: 401 Sunset Avenue, Windsor, ON, N9B3P4, Canada
Keywords: shrinkage estimator ; pre-test estimator ; Least square estimator ; Regression models
Abstract:

In this talk, we consider multivariate regression models where the regression vector of coefficients in the can be partitioned into two sub-vectors as main effects (e.g. treatment effect, genetic effects) and for 'nuisance' effects (e.g., age, lab). In this situation, inference about main effect may benefit from moving the least squares estimate for the initial model in the direction of the least squares estimate without the nuisance variables (Steinian shrinkage), or to drop the nuisance variables if there is evidence that they do not provide useful information (pre-testing). We investigate the large-sample properties of shrinkage and pretest estimators under quadratic loss and show that under general conditions a shrinkage type estimator improves on the initial model benchmark least square estimator.

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