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Activity Number:
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289
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Type:
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Contributed
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Date/Time:
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Tuesday, August 8, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #306777 |
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Title:
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Testing Equality of Covariance Matrices When Data Are Incomplete
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Author(s):
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Mortaza Jamshidian*+ and James Schott
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Companies:
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California State University, Fullerton and University of Central Florida
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Address:
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Department of Mathematics, Fullerton, CA, 92834,
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Keywords:
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likelihood ratio test ; missing completely at random ; robust tests ; test of homogeneity of covariance matrices ; Wald test ; missing data
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Abstract:
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In this talk we propose statistical tests for equality of covariance matrices when data are missing, a problem that seems not have received attention in a general setting. A Wald test and a likelihood ratio test (LRT), based on the assumption of normal populations are developed. As in the complete data case, these tests perform poorly for non-normal data. This has led us to construct a robust Wald test for our problem. A simulation study is carried out to assess the performance of the proposed tests in terms of their observed significance level and the power. It is found that one of our tests performs particularly well for both normal and non-normal data in both small and large samples. In addition to their usual applications, we have discussed the application of the proposed tests in testing whether a set of data are missing completely at random.
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