A0386
Title: Robust estimation of the high-dimensional precision matrix
Authors: Zhaoxue Tong - Florida State University (United States) [presenting]
Runze Li - The Pennsylvania State University (United States)
Abstract: Estimating the precision matrix (inverse of the covariance matrix) in high-dimensional settings is crucial for various applications, such as Gaussian graphical models and linear discriminant analysis. A robust and computationally efficient estimator is proposed for high-dimensional heavy-tailed data. Building upon winsorized rank-based regression, the method offers robustness without sacrificing computational tractability. The statistical consistency of the estimator is established, with a focus on conditions required for the error variance estimator in winsorized rank-based regression. For sub-Gaussian data, the sample variance meets the criteria, while for heavy-tailed data, a robust variance estimator based on the median-of-means approach is proposed. Simulation studies and real data analysis show that the proposed method performs well compared with existing works.
