13 papers
Boot-AVT: Asymptotically Anytime-Valid Testing with Degenerate U-Statistics via Bootstrap
Jixuan Liu, Long Feng, Liuhua Peng +1
Many nonparametric tests are based on -statistics that are degenerate under the null hypothesis. We develop Boot-AVT, a bootstrap procedure for asymptotically anytime-valid test…
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…
High Dimensional Bootstrap and Asymptotic Expansion for the -th Largest Coordinate
Long Feng
We study bootstrap inference for the th largest coordinate of a normalized sum of independent high-dimensional random vectors. Existing second-order theory for maxima does not d…
Difference-Based High-Dimensional Long-Run Covariance Matrix Estimation for Mean-shift Time Series
Yanhong Liu, Fengyi Song, Long Feng
We consider estimation of high-dimensional long-run covariance matrices for time series with nonconstant means, a setting in which conventional estimators can be severely biased. T…
Conditional Rank-Rank Regression via Deep Conditional Transformation Models
Xiaoyi Wang, Long Feng, Zhaojun Wang
Intergenerational mobility quantifies the transmission of socio-economic outcomes from parents to children. While rank-rank regression (RRR) is standard, adding covariates directly…
High dimensional Mean Test for Temporal Dependent Data
Yuchen Hu, Xiaoyi Wang, Long Feng
This paper proposes a novel test method for high-dimensional mean testing regard for the temporal dependent data. Comparison to existing methods, we establish the asymptotic normal…
Adaptive Change Point Inference for High Dimensional Time Series with Temporal Dependence
Xiaoyi Wang, Jixuan Liu, Long Feng
This paper investigates change point inference in high-dimensional time series. We begin by introducing a max--norm based test procedure, which demonstrates strong performance…
Spatial-Sign based High dimensional Change Point Inference
Jixuan Liu, Long Feng, Liuhua Peng +1
High-dimensional changepoint inference, adaptable to diverse alternative scenarios, has attracted significant attention in recent years. In this paper, we propose an adaptive and r…