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Abstract Details
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Activity Number:
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476
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #301634 |
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Title:
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Test for High-Dimensional Covariance Matrices with Banded Structure
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Author(s):
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Yumou Qiu*+ and Song Xi Chen
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Companies:
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Iowa State University and Iowa State University
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Address:
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4211 Lincoln Swing st Unit 20, Ames, IA, 50014,
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Keywords:
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High data dimension ;
Banded covariance structure ;
Large p, small n
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Abstract:
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The paper proposes a nonparametric test for the banded structure of high dimensional covariance matrices, which corresponds to the situations where the variables far apart in the ordering are uncorrelated. We do not assume any specific parametric distribution for the data. The test is robust under the situations where the data dimension is much larger than the sample size, namely the "large p, small n" situations. Specifically, the method can be used to test whether the covariance matrices are diagonal, which means all the variables are mutually uncorrelated. Also, the paper proposes a method to estimate the bandwidth of a covariance matrix which is truly banded. We demonstrate by both theoretical and simulation studies that the test has good properties for a wide range of dimensions and sample sizes.
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