A Class of Stein-Rules in Multivariate Regression Model with Structural Changes

• Activity Number: 317
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: SSC
• Author(s): Fuqi Chen* and Sévérien Nkurunziza
• Companies: Fields Institute and University of Windsor/Université de Sherbrooke
• Keywords: multivariate regression ; mixingale array ; change-points ; shrinkage estimators ; ADR

Abstract

In this talk, we consider an estimation problem of the matrix of the regression coefficients in multivariate regression models with unknown change-points. Under general conditions, we propose a class of estimators which includes as special cases shrinkage estimators(SEs) as well as the unrestricted estimator(UE) and the restricted estimator(RE) under some linear restrictions. We also derive a more general condition for the SEs to dominate the UE. To this end, we extend some results underlying the multidimensional version of Mixingale Central Limit Theorem as well as some important identities for deriving the risk function of SEs. Finally, we present some simulation studies, which corroborate the theoretical findings.