1 citations · 1 across the 6 of their papers we have counts for
6 papers
Spatial-sign-based multilinear principal component analysis for tensor data
Dongxu Yang, Wanfeng Liang, Le Zhou +1
Multilinear principal component analysis (MPCA) reduces the dimension of tensor-valued data while preserving their mode-specific structure, but its quadratic scatter criterion can…
High-Dimensional Change Point Analysis for Temporally Dependent Data
Xiaoyi Wang, Le Zhou, Jixuan Liu +1
This paper develops adaptive procedures for detecting and locating mean changes in high-dimensional time series. Quadratic CUSUM statistics target dense changes, whereas coordinate…
Elliptical Regularized Hotelling Testing for High Dimensional Data
Long Feng, Le Zhou, Xiaoyi Wang
We consider one-sample testing of a high-dimensional location parameter under elliptically symmetric distributions with heavy tails and pervasive cross-sectional dependence. We pro…
Cauchy Aggregation of Ridge-Regularized Hotelling Tests for High-Dimensional Change-Point Detection
Ping Zhao, Le Zhou, Long Feng
Ridge-regularized Hotelling-type (RHT) change-point tests depend on a ridge parameter , but the power-optimal value is determined by the unknown covariance structure and the unk…
Rank-Based Sparse Regression in Principal Components Space under Measurement Error
Long Feng, Xiaoyi Wang, Le Zhou
We study high-dimensional regression in principal components space when the predictors are observed with additive measurement error and the response errors may be heavy-tailed. The…
Enveloped Huber Regression
Le Zhou, R. Dennis Cook, Hui Zou
Huber regression (HR) is a popular robust alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the envelop…