Publications

Preprints (arXiv)

• Online quantile regression. (with Y. Shen & D. Xia) [arXiv preprint]

• On inference for the support vector machine. (with J. Rybak & H. Battey) [preprint]

• High-dimensional expected shortfall regression. (with S. Zhang, X. He & K. M. Tan) [arXiv preprint]

• Huber principal component analysis for large-dimensional factor models. (with Y. He, L. Li & D. Liu) [arXiv preprint]

• An efficient iterative least squares algorithm for large-dimensional matrix factor model via random projection. (with Y. He & R. Zhao) [arXiv preprint]

• Matrix Kendall's tau in high dimensions: A robust statistic for matrix factor model. (with Y. He, Y. Wang, L. Yu & W. Zhou) [arXiv preprint]

• A unified framework for testing high dimensional parameters: A data-adaptive approach. (with C. Zhou, X. Zhang & H. Liu) [arXiv preprint]

2024+

• Private optimal inventory policy learning for feature-based newsvendor with unknown demand. (with T. Zhao & L. Wang) Management Science, to appear. [arXiv preprint]

• How do noise tails impact on deep ReLU networks? (with J. Fan & Y. Gu) The Annals of Statistics, to appear. [arXiv preprint]

• Gaussian differentially private robust mean estimation and inference. (with M. Yu & Z. Ren) Bernoulli, to appear. [preprint] [supplement]

• A unified algorithm for penalized convolution smoothed quantile regression. (with R. Man, X. Pan & K. M. Tan) Journal of Computational and Graphical Statistics 33(2): 625-637. [DOI:10.1080/10618600.2023.2275999] [R package]

• Low-rank matrix recovery under heavy-tailed errors. (with M. Yu & Q. Sun) Bernoulli 30(3): 2326-2345. [DOI:10.3150/23-BEJ1675] [supplement]

• Retire: Robustified expectile regression in high dimensions. (with R. Man, K. M. Tan & Z. Wang)  Journal of Econometrics  239(2): 105459. [DOI:10.1016/j.jeconom.2023.04.004] [supplement]

• Transfer learning for high-dimensional quantile regression with statistical guarantee. (with S. Qiao & Y. He) Transactions on Machine Learning Research 01/2024. [url]

2023

• Robust estimation and inference for expected shortfall regression with many regressors. (with X. He & K. M. Tan) Journal of the Royal Statistical Society, Series B 85(4): 1223-1246. [DOI:10.1093/jrsssb/qkad063] [preprint]

• High-dimensional composite quantile regression: Optimal statistical guarantees and fast algorithms. (with H. Moon) Electronic Journal of Statistics 17(2): 2067-2119. [DOI:10.1214/23-EJS2147] [Python code]

• Smoothed quantile regression with large-scale inference. (with X. He, X. Pan & K. M. Tan) Journal of Econometrics 232(2): 367-388. [DOI:10.1016/j.jeconom.2021.07.010] [R] [Python] [slides]

• Large-scale inference of multivariate regression for heavy-tailed and asymmetric data. (with Y. Song & W. Zhou) Statistica Sinica 33(3): 1831-1852. [DOI:10.5705/ss.202021.0003]

2022

• Scalable estimation and inference for censored quantile regression process. (with X. He, X. Pan & K. M. Tan) The Annals of Statistics 50(5): 2899-2924. [DOI: 10.1214/22-AOS2214] [supplement] [R code]

• Communication-constrained distributed quantile regression with optimal statistical guarantees. (with K. M. Tan & H. Battey) Journal of Machine Learning Research 23(272): 1-61. [jmlr.org]

• High-dimensional quantile regression: Convolution smoothing and concave regularization. (with K. M. Tan & L. Wang) Journal of the Royal Statistical Society, Series B 84(1): 205-233.  [DOI:10.1111/rssb.12485] [supplement] [R] [Python]

• Distributed adaptive Huber regression. (with J. Luo & Q. Sun) Computational Statistics & Data Analysis 169 107419. [DOI:10.1016/j.csda.2021.107419] [preprint]

• On the asymptotic distribution of the scan statistic for empirical distributions. (with A. Ying) Extremes 25 87-528. [DOI:10.1007/s10687-021-00435-1] [pdf]

2021

• Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design. (with X. Pan) Information and Inference: A Journal of the IMA 10(3): 813-861. [DOI:10.1093/imaiai/iaaa006] [R]

• A new principle for tuning-free Huber regression. (with L. Wang, C. Zheng & W. Zhou) Statistica Sinica 31(4): 2153-2177. [DOI:10.5705/ss.202019.0045] [supplement] [R] [slides]

• Iteratively reweighted ℓ1-penalized robust regression. (with X. Pan & Q. Sun) Electronic Journal of Statistics 15(1): 3287-3348. [DOI:10.1214/21-EJS1862] [R] [Python]

2020

• Robust inference via multiplier bootstrap. (with X. Chen) The Annals of Statistics 48(3): 1665-1691. [DOI:10.1214/19-AOS1863] [supplement] [Matlab]

• Adaptive Huber regression. (with Q. Sun & J. Fan) Journal of the American Statistical Association 115(529): 254-265. [DOI:10.1080/01621459.2018.1543124] [arXiv.org] [R]

• FarmTest: An R package for factor-adjusted robust multiple testing  (with K. Bose, J. Fan, Y. Ke & X. Pan) The R Journal 12(2): 372-387. [DOI:10.32614/RJ-2021-023]

2019

• FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control. (with J. Fan, Y. Ke & Q. Sun) Journal of the American Statistical Association 114(528): 1880-1893. [DOI:10.1080/01621459.2018.1527700] [arXiv.org] [R]

• User-friendly covariance estimation for heavy-tailed distributions. (with Y. Ke, S. Minsker, Z. Ren & Q. Sun) Statistical Science 34(3): 454-471. [DOI:10.1214/19-STS711]

2018

• A new perspective on robust M-estimation: Finite sample theory and applications to dependence-adjusted multiple testing. (with K. Bose, J. Fan & H. Liu) The Annals of Statistics 46(5): 1904-1931. [DOI:10.1214/17-AOS1606]

• Are discoveries spurious? Distributions of maximum spurious correlations and their applications. (with J. Fan & Q.-M. Shao) The Annals of Statistics 46(3): 989-1017. [DOI:10.1214/17-AOS1575] [slides]

• Max-norm optimization for robust matrix recovery. (with E. X. Fang, H. Liu & K.-C. Toh) Mathematical Programming 167, 5-35. [DOI:10.1007/s10107-017-1159-y]

• On Gaussian comparison inequality and its application to spectral analysis of large random matrices. (with F. Han & S. Xu) Bernoulli 24(3): 1787-1833. [DOI:10.3150/16-BEJ912]

• Principal component analysis for big data. (with J. Fan, Q. Sun & Z. Zhu) Wiley StatsRef: Statistics Reference Online. [DOI:10.1002/9781118445112.stat08122] [pdf] 

2017

• Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity. (with J. Chang, C. Zheng & W. Zhou)  Biometrics 73(4): 1300-1310. [DOI:10.1111/biom.12695] [slides]

• Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering. (with J. Chang, W. Zhou and L. Wang) Biometrics 73(1): 31-41. [DOI:10.1111/biom.12552]

• Two-sample smooth tests for the equality of distributions. (with C. Zheng & Z. Zhang)  Bernoulli 23(2): 951-989. [DOI:10.3150/15-BEJ766]

• Self-normalization: Taming a wild population in a heavy-tailed world. (with Q.-M. Shao)  Appl. Math. J. Chinese Univ. 32(3): 253-269. [DOI:10.1007/s11766-017-3552-y]

2016

• Guarding against spurious discoveries in high dimensions. (with J. Fan)  Journal of Machine Learning Research 17(203): 1-34. [jmlr.org] [slides]

• Nonparametric covariate-adjusted regression. (with A. Delaigle & P. Hall) The Annals of Statistics 44(5): 2190-2220. [DOI:10.1214/16-AOS1442]

• Cramér-type moderate deviations for Studentized two-sample U-statistics with applications. (with J. Chang & Q.-M. Shao) The Annals of Statistics 44(5): 1931-1956. [DOI:10.1214/15-AOS1375] [slides]

• Matrix completion via max-norm constrained minimization. (with T. T. Cai) Electronic Journal of Statistics 10(1): 1493-1525. [DOI:10.1214/16-EJS1147]

• Cramér type moderate deviation theorems for self-normalized processes. (with Q.-M. Shao) Bernoulli 22(4): 2029-2079. [DOI:10.3150/15-BEJ719]

• Stein's method for nonlinear statistics: A brief survey and recent progress. (with Q.-M. Shao & K. Zhang) Journal of Statistical Planning and Inference 168, 68-89. [DOI:10.1016/j.jspi.2015.06.008]