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Bootstrapping Spectral Statistics in High Dimensions

Banff International Research Station for Mathematical Innovation and Discovery

Spectral statistics play a central role in many multivariate testing problems. It is therefore of interest to approximate the distribution of functions of the eigenvalues of sample covariance matrices. Although bootstrap methods are an established approach to approximating the laws of spectral statistics in low-dimensional problems, these methods are relatively unexplored in the high-dimensional setting. The aim of this paper is to focus on linear spectral statistics as a class of prototype statistics for developing a new bootstrap in the high-dimensional setting â and we refer to this method as the "Spectral Bootstrap". In essence, the method originates from the parametric bootstrap, and is motivated by the notion that, in high dimensions, it is difficult to obtain a non-parametric approximation to the full data-generating distribution. From a practical standpoint, the method is easy to use, and allows the user to circumvent the difficulties of complex asymptotic formulas for linear spectral statistics. In addition to proving the consistency of the proposed method, we provide encouraging empirical results in a variety of settings. Lastly, and perhaps most interestingly, we show through simulations that the method can be applied successfully to statistics outside the class of linear spectral statistics, such as the largest sample eigenvalue and others.

Mathematics; Statistics

video/mp4

Author affiliation: University of California at Davis

2019-10-09

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/71867

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/