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Activity Number: 51 - Large-Scale Global and Simultaneous Inference
Type: Invited
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract #321868
Title: Hypothesis Testing of Matrix Graph Model
Author(s): Yin Xia*
Companies: Fudan University
Keywords: Brain connectivity analysis ; False discovery rate ; Gaussian graphical model ; Matrix-variate normal distribution ; Multiple testing
Abstract:

In this talk, we adopt a matrix normal distribution framework and formulate the brain connectivity analysis as a precision matrix hypothesis testing problem. Based on the separable spatial-temporal dependence structure, we develop oracle and data-driven procedures to test both the global hypothesis that all spatial locations are conditionally independent, and simultaneous tests for identifying conditional dependent spatial locations with false discovery rate control. Our theoretical results show that the data-driven procedures perform asymptotically as well as the oracle procedures and enjoy certain optimality properties. The empirical finite-sample performance of the proposed tests is studied via intensive simulations, and the new tests are applied on a real electroencephalography data analysis.


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

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