JSM 2011 Online Program

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Abstract Details

Activity Number: 512
Type: Contributed
Date/Time: Wednesday, August 3, 2011 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #300891
Title: Inner Envelopes: Efficient Estimation in Multivariate Linear Regression
Author(s): Zhihua Su*+ and R. Dennis Cook
Companies: University of Minnesota and University of Minnesota
Address: 472 Ford Hall , Minneapolis, MN, 55455, United States
Keywords: Dimension reduction ; Envelope model ; Reducing subspaces ; Grassmann manifolds

In this paper, we propose a new class of models, called the inner envelope model which leads to efficient estimation in the context of multivariate linear regression. The asymptotic distribution and the consistency of its maximum likelihood estimators are established. Simulation studies and examples both show that the efficiency gains can be massive. Compared to the envelope model introduced by Cook et al. (2010), the inner envelope model has a different starting point on the mechanism of improving the efficiency, and it can produce substantial gains in the situations where the envelope model offers no gains. The construction of the inner envelope model also opens a new frontier on the way in which reducing subspaces can be used to improve efficiency in multivariate problems.

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