5 papers
Rank-Based Tests for Mutual Independence of High-Dimensional Random Vectors via Norm
Ping Zhao, Hongfei Wang, Long Feng
We consider the problem of testing mutual independence among the components of a high-dimensional random vector. Building on the rank-based max-sum framework, we introduce fixed fi…
High-Dimensional Two-Sample Test for Elliptical Symmetry Distribution
Long Feng, Hongfei Wang
We study the high-dimensional two-sample location problem under elliptical symmetry with arbitrary dependence in the scatter matrix. Existing spatial-sign procedures are attractive…
Robust Spatial-Sign-Based Testing of High-Dimensional Alpha in Conditional Factor Models
Ping Zhao, Hongfei Wang
This paper develops a new framework for alpha testing in high-dimensional factor pricing models with time-varying coefficients. To detect sparse alternatives, we propose a spatial-…
High dimensional matrix estimation through elliptical factor models
Xinyue Xu, Huifang Ma, Hongfei Wang +1
Elliptical factor models play a central role in modern high-dimensional data analysis, particularly due to their ability to capture heavy-tailed and heterogeneous dependence struct…
Spatial Sign based Principal Component Analysis for High Dimensional Data
Ping Zhao, Hongfei Wang, Long Feng
This article focuses on the robust principal component analysis (PCA) of high-dimensional data with elliptical distributions. We investigate the PCA of the sample spatial-sign cova…