Abstract Details
Activity Number:
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419
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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SSC
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Abstract #312180
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Title:
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Optimal Method in Multiple Regression with Structural Changes
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Author(s):
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Sévérien Nkurunziza*+ and Fuqi Chen
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Companies:
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University of Windsor and University of Windsor
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Keywords:
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ADB ;
ADR ;
change-points ;
multiple regression ;
restricted estimator ;
shrinkage estimators
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Abstract:
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In this paper, we consider an estimation problem in regression model with several unknown change-points, when an uncertain prior information is available. In particular, the regression coefficients are suspected to be restricted to a certain subspace while the "change-points" are treated as nuisance parameters to be estimated as well. Also, we relax some assumptions which are commonly given in literature about the linear model with change-points, and under these realistic assumptions, we propose a class of estimators which includes as a special cases shrinkage estimators (SEs) as well as the unrestricted estimator (UE) and the restricted estimator (RE). We also derive a more general condition for the SEs to dominate the UE. To this end, we generalize some identities for the evaluation of the bias and risk functions of shrinkage-type estimators. The proposed methodology works for both matrix and vector parameters cases. Finally, in order to illustrate the performance of the proposed method, we present some simulation studies.
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Authors who are presenting talks have a * after their name.
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