STATSCALE SEMINARS
PREVIOUS SEMINAR - 6th November 2020

Speaker: Alex Aue (UC Davis)

Title: Random matrix theory aids statistical inference in high dimensions

Abstract: The first part of the talk is on bootstrapping spectral statistics in high dimensions. 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 talk is to focus on linear spectral statistics (LSS) as a class of "prototype statistics" for developing a new bootstrap method in the high-dimensional setting. 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 LSS. In addition to proving the consistency of the proposed method, I will discuss encouraging empirical results in a variety of settings. Lastly, and perhaps most interestingly, simulations indicate that the method can be applied successfully to statistics outside the class of LSS, such as the largest sample eigenvalue and others.

The second part of the talk briefly highlights two-sample tests in high dimensions by discussing ridge-regularized generalization of Hotelling's T^2. The main novelty of this work is in devising a method for selecting the regularization parameter based on the idea of maximizing power within a class of local alternatives. The performance of the proposed test procedures will be illustrated through an application to a breast cancer data set where the goal is to detect the pathways with different DNA copy number alterations across breast cancer subtypes.

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