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Activity Number: 294
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #318833 View Presentation
Title: Testing Independence with High-Dimensional Correlated Samples
Author(s): Xi Chen* and Weidong Liu
Companies: and Shanghai Jiao Tong University
Keywords: testing independence ; matrix-variate ; minimax ; covaraince ; high-dimensional ; power
Abstract:

Testing independence among a number of (ultra) high-dimensional random samples is an fundamental and challenging problem. By arranging n identically distributed p-dimensional random vectors into a p by n data matrix, we investigate the testing problem on independence among columns under the matrix-variate normal modeling of the data. We propose a computationally simple and tuning free test statistic, characterize its limiting null distribution, analyze the statistical power and prove its minimax optimality. As an important by-product of the test statistic, a ratio-consistent estimator for the quadratic functional of covariance matrix from correlated samples is developed.


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

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