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Activity Number: 587
Type: Invited
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #318235
Title: Bootstrapping Spectral Statistics in High Dimensions
Author(s): Miles Lopes*
Companies: University of California at Davis
Keywords: bootstrap ; High Dimensions ; Covariance Matrices ; Random Matrices ; Multivariate Analysis ; Hypothesis Testing

In many multivariate testing problems, it is necessary to know the distribution of certain functions of the eigenvalues of sample covariance matrices (i.e. spectral statistics). Although bootstrap methods are a well established approach to approximating the laws of spectral statistics in low-dimensional problems, their extension to the high-dimensional setting is relatively unexplored. In our work, we show how a decay constraint in the population spectrum can lead to consistent-in-law approximations of spectral statistics by way of the bootstrap, even when the problem is high-dimensional.

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

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