11 papers · 1 filter
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…
Factor-Adjusted Location Tests for High-Dimensional Time Series
Jiyang Wang, Xifen Huang, Long Feng
We study high-dimensional one-sample mean testing for time series with strong common serial dependence driven by latent dynamic factors. After estimating the dynamic factor loading…
Elliptical Regularized Hotelling Tests for High-Dimensional Change-Point Detection
Fengyi Song, Mengtao Wen, Long Feng
We propose an elliptical regularized Hotelling (ERHT) procedure for detecting location changes in high-dimensional sequences with heavy-tailed, cross-sectionally dependent observat…
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…
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…