Adaptive Est. of Large Covariance Matrices

Primary tabs


TITLE:   Adaptive Estimation of Large Covariance Matrices

SPEAKER:  Dr. Ming Yuan


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.


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


No keywords were submitted.

Target Audience