Abstract Details
Activity Number:
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195
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Type:
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Contributed
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Date/Time:
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Monday, August 10, 2015 : 10:30 AM to 12:20 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #317968
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Title:
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More Powerful Tests for Sparse High-Dimensional Covariances
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Author(s):
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Liuhua Peng* and Song Xi Chen and Wen Zhou
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Companies:
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Iowa State University and Peking University/Iowa State Univeristy and Colorado State University
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Keywords:
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Banding estimation ;
High-dimensional inference ;
Hypothesis testing ;
Sparse Covariance matrix
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Abstract:
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This paper considers improving the power of tests for the identity and sphericity hypotheses regarding high dimensional covariance matrices. The power improvement is achieved by employing the banding estimator for the covariance matrices, which leads to signicant reduction in the variance of the test statistics in high dimension. Theoretical justication and simulation experiments are provided to ensure the validity of the proposed tests. The tests are used to analyze an dataset from an acute lymphoblastic leukemia gene expression study.
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Authors who are presenting talks have a * after their name.
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