High dimensional inverse covariance matrix estimation

Event Details
  • Date/Time:
    • Thursday October 7, 2010
      11:00 am - 12:00 pm
  • Location: ISyE Executive classroom
  • Phone:
  • URL:
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
No contact information submitted.
Summaries

Summary Sentence: High dimensional inverse covariance matrix estimation

Full Summary: No summary paragraph submitted.

TITLE: High dimensional inverse covariance matrix estimation

SPEAKER:  Ming Yuan

ABSTRACT:

More and more often in practice, one needs to estimate a high dimensional covariance matrix. In this talk, we discuss how this task is often related to the sparsity of the inverse covariance matrix. In particular, we consider estimating a (inverse) covariance matrix that can be well approximated by ``sparse'' matrices. Taking advantage of the connection between multivariate linear regression and entries of the inverse covariance matrix, we introduce an estimating procedure that can effectively exploit such ``sparsity''.  The proposed method can be computed using linear programming and therefore has the potential to be used in very high dimensional problems. Oracle inequalities are established for the estimation error in terms of several operator norms, showing that the method is adaptive to different types of sparsity of the problem.

Additional Information

In Campus Calendar
No
Groups

H. Milton Stewart School of Industrial and Systems Engineering (ISYE)

Invited Audience
No audiences were selected.
Categories
No categories were selected.
Keywords
No keywords were submitted.
Status
  • Created By: Anita Race
  • Workflow Status: Published
  • Created On: Oct 6, 2010 - 5:15am
  • Last Updated: Oct 7, 2016 - 9:52pm