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Adaptive Est. of Large Covariance Matrices
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STATISTICS SEMINAR
TITLE: Adaptive Estimation of Large Covariance Matrices
SPEAKER: Dr. Ming Yuan
ABSTRACT:
Estimation of large covariance matrices has drawn considerable recent attention and the theoretical focus so far is mainly on developing a minimax theory over a fixed parameter space. In this talk, I shall discuss adaptive covariance matrix estimation where the goal is to construct a single procedure which is minimax rate optimal simultaneously over each parameter space in a large collection. The estimator is constructed by carefully dividing the sample covariance matrix into blocks and then simultaneously estimating the entries in a block by thresholding. I shall also illustrate the use of the technical tools developed in other matrix estimation problems.
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- Workflow Status: Published
- Created By: Anita Race
- Created: 04/02/2012
- Modified By: Fletcher Moore
- Modified: 10/07/2016
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