Abstract Details
Activity Number:
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559
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #311488
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View Presentation
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Title:
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Sure Based Banding/Tapering Parameter Choice for the Regularized Estimation of Large Covariance Matrices
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Author(s):
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Danning Li*+ and Hui Zou
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Companies:
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and University of Minnesota
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Keywords:
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Frobenius risk ;
Regularized estimator ;
Consistency ;
Covariance matrix ;
SURE ;
High dimension
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Abstract:
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Bandable covariance matrices are often used to model the dependence structure of variables that follow a natural order. The banding/tapering estimator gives a good estimation of such bandable covariance matrices in high dimensional case. The choice of the banding/tapering parameter influences the estimation risk significantly. In this paper, we develop a generalized Stein's Unbiased Risk Estimation (GSURE) to estimate the generalized Frobenius risk of the banding/tapering estimator. We establish the limiting distribution and concentration inequality of the GSURE formula. These asymptotic results lead to a tuning procedure to determine a good banding/tapering parameter. We also show that the chosen banding/tapering parameter is equal to the true one almost surely under certain circumstance. Simulations are used to demonstrate the performance of this new tuning method.
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Authors who are presenting talks have a * after their name.
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