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Activity Number: 195
Type: Contributed
Date/Time: Monday, August 10, 2015 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #317968
Title: More Powerful Tests for Sparse High-Dimensional Covariances
Author(s): Liuhua Peng* and Song Xi Chen and Wen Zhou
Companies: Iowa State University and Peking University/Iowa State Univeristy and Colorado State University
Keywords: Banding estimation ; High-dimensional inference ; Hypothesis testing ; Sparse Covariance matrix
Abstract:

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.

Authors who are presenting talks have a * after their name.

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