I am an Associate Professor in the Department of Information and Decision Sciences at the College of Business Administration, University of Illinois at Chicago [CV].
My recent research interests center around the development and analysis of statistical methods and optimization tools for structured high-dimensional data problems, including sparse regression, low-rank, and nonparametric neural networks. The main focus is to develop robust and quantile-based methods in settings where the error distribution is heavy-tailed and/or heteroscedastic. I also work on developing and analyzing methods (from a statistical perspective) with nontraditional data types, such as distributed data, streaming/online data, multi-source data, and data subject to privacy concerns. A full list of my publications is accessible through Google Scholar.
Contact
Phone: (312) 355 0246
E-mail: wenxinz@uic.edu
Office: University Hall 2423
Research Interests
High-dimensional data analysis
Robust learning for heavy-tailed data
Non/semi-parametric statistics
Neural networks and deep learning
Quantile and expected shortfall regression
Editorial Service
Associate Editor for JRSS-B (01/2022-Present)
Associate Editor for Annals of Statistics (01/2022-Present)
Associate Editor for Annals of Applied Probability (01/2022-Present)
Associate Editor for Statistics: A Jnl of Theor. & Appl. Stat (07/2020-08/2023)
Recent Selected Publications
Robust estimation and inference for expected shortfall regression with many regressors. (with X. He & K. M. Tan) J. R. Stat. Soc. B. 85(4): 1223-1246. [DOI] [Python]
Smoothed quantile regression with large-scale inference. (with X. He, X. Pan & K. M. Tan) J. Econom. 232(2): 367-388. [DOI] [R] [Python]
Scalable estimation and inference for censored quantile regression process. (with X. He, X. Pan & K. M. Tan) Ann. Statist. 50(5): 2899-2924. [DOI] [supplement] [R]
High-dimensional quantile regression: Convolution smoothing and concave regularization. (with K. M. Tan & L. Wang) J. R. Stat. Soc. B. 84(1): 205-233. [DOI] [supplement] [Python] [R]
Communication-constrained distributed quantile regression with optimal statistical guarantees. (with K. M. Tan & H. Battey) J. Mach. Learn. Res. 23(272): 1-61. [jmlr.org]
A new principle for tuning-free Huber regression. (with L. Wang, C. Zheng & W. Zhou) Stat. Sin. 31(4): 2153-2177. [DOI] [supplement]
Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design. (with X. Pan) Inf. Inference 10(3): 813-861. [DOI]
Iteratively reweighted ℓ1-penalized robust regression. (with X. Pan & Q. Sun) Electron. J. Stat. 15(1): 3287-3348. [DOI]
Robust inference via multiplier bootstrap. (with X. Chen) Ann. Statist. 48(3): 1665-1691. [DOI] [Matlab]
Adaptive Huber regression. (with Q. Sun & J. Fan) J. Amer. Statist. Assoc. 115(529): 254-265. [DOI] [R]
FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control. (with J. Fan, Y. Ke & Q. Sun) J. Amer. Statist. Assoc. 114(528): 1880-1893. [DOI] [R]
A new perspective on robust M-estimation: Finite sample theory and applications to dependence-adjusted multiple testing. (with K. Bose, J. Fan & H. Liu) Ann. Statist. 46(5): 1904-1931. [DOI]