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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.

Status

  • Workflow Status:Published
  • Created By:Anita Race
  • Created:04/02/2012
  • Modified By:Fletcher Moore
  • Modified:10/07/2016

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