This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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48
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Type:
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Invited
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Date/Time:
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Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
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Sponsor:
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JCGS-Journal of Computational and Graphical Statistics
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Abstract - #306151 |
Title:
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Large Gaussian Covariance Matrix Estimation with Markov Structures
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Author(s):
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Xinwei Deng*+ and Ming Yuan
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Companies:
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Georgia Institute of Technology and University of Wisconsin
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Address:
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, , ,
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Keywords:
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Conditional independence ;
GraphGarrote ;
Markov property
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Abstract:
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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
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Authors who are presenting talks have a * after their name.
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