This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 48
Type: Invited
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: JCGS-Journal of Computational and Graphical Statistics
Abstract - #306151
Title: Large Gaussian Covariance Matrix Estimation with Markov Structures
Author(s): Xinwei Deng*+ and Ming Yuan
Companies: Georgia Institute of Technology and University of Wisconsin
Address: , , ,
Keywords: Conditional independence ; GraphGarrote ; Markov property
Abstract:

Covariance matrix estimation for a large number of Gaussian random variables is a challenging yet increasingly common problem. A fact neglected in practice is that the random variables are frequently observed with certain temporal or spatial structures. Such a problem arises naturally in many practical situations with time series and images as the most popular and important examples. Effectively accounting for such structures not only results in more accurate estimation but also leads to models that are more interpretable. In this article, we propose shrinkage estimators of the covariance matrix speci?cally to address this issue. The proposed methods exploit sparsity in the inverse covariance matrix in a systematic fashion so that the estimate conforms with models of Markov structure and is amenable for subsequent stochastic modeling. The present approach complements the exis


The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2010 program




2010 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.