Department of Information and Decision Sciences
College of Business Administration
University of Illinois at Chicago
601 S. Morgan St.
Chicago, IL 60607
Phone: (312) 355 0246
E-mail: wenxinz@uic.edu
Office: University Hall 2423
I am an Associate Professor in the Department of Information and Decision Sciences within the College of Business Administration at the University of Illinois Chicago [Google Scholar & CV].
My recent research interests center around the development and analysis of statistical methods (estimation and inference) and optimization tools for structured high-dimensional data problems, including sparse regression, low-rank, and nonparametric models. 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.
P.S. I don't respond to email requests for recommendation letters unless I know you well. If you were rarely in my class (e.g. MATH 189), it's clear that I don't know you well.
with
Journal of the Royal Statistical Society: Series B, 85(4): 1223–1246, 2023
[DOI] [preprint] [slides]
Smoothed quantile regression with large-scale inference
with
Journal of Econometrics, 232(2): 367-388, 2023
[DOI] [R package] [Python code] [slides]
Scalable estimation and inference for censored quantile regression process
with
The Annals of Statistics, 50(5): 2899-2924, 2022
[DOI] [supplement] [R code]
Communication-constrained distributed quantile regression with optimal statistical guarantees
with
Journal of Machine Learning Research, 23(272): 1-61, 2022
[DOI]
High-dimensional quantile regression: convolution smoothing and concave regularization
with
Journal of the Royal Statistical Society: Series B, 84(1): 205-233, 2022
[DOI] [supplement] [R package] [Python code]
Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design
with
Information and Inference: A Journal of the IMA, 10, 813-861, 2021
[DOI] [R code]
A new principle for tuning-free Huber regression
with
Statistica Sinica, 31, 2153-2177, 2021
[DOI] [supplement] [R package]
Iteratively reweighted l1-penalized robust regression
with
Electronic Journal of Statistics, 15, 3287-3348, 2021
[DOI] [R package] [Python code]
Robust inference via multiplier bootstrap
with
The Annals of Statistics, 48, 1665-1691, 2020
[DOI] [supplement] [Matlab code]
Adaptive Huber regression
with
Journal of the American Statistical Association, 115, 254-265, 2020
[DOI] [arXiv] [R package] [slides]
FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control
with
Journal of the American Statistical Association, 114, 1880-1893, 2019
[DOI] [R package]
User-friendly covariance estimation for heavy-tailed distributions
with
Statistical Science, 34, 454-471, 2019
[DOI]
A new perspective on robust M-estimation: Finite sample theory and applications to dependence-adjusted multiple testing
with
The Annals of Statistics, 46, 1904-1931, 2018
[DOI] [arXiv]
Are discoveries spurious? Distributions of maximum spurious correlations and their applications
with
The Annals of Statistics, 46, 989-1017, 2018
[DOI] [arXiv] [slides]
Max-norm optimization for robust matrix recovery
with
Mathematical Programming, Series B, 167, 5-35, 2018
[DOI] [arXiv]
Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity
with
Biometrics, 73, 1300-1310, 2017
[DOI] [arXiv] [slides]
Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering
with
Biometrics, 73, 31-41, 2017
[DOI] [arXiv]
Guarding against spurious discoveries in high dimensions
with
Journal of Machine Learning Research, 17(203), 1-34, 2016
[DOI] [arXiv] [slides]
Nonparametric covariate-adjusted regression
with
The Annals of Statistics, 44, 2190-2220, 2016
[DOI]
Cramér-type moderate deviations for Studentized two-sample U-statistics with applications
with
The Annals of Statistics, 44, 1931-1956, 2016
[DOI] [slides]
Matrix completion via max-norm constrained optimization
with
Electronic Journal of Statistics, 10, 1493-1525, 2016
[DOI]
Cramér type moderate deviation theorems for self-normalized processes
with
Bernoulli, 22, 2029-2079, 2016
[DOI]
Nonparametric and parametric estimators of prevalence from group testing data with aggregated covariates
with
Journal of the American Statistical Association, 110, 1785-1796, 2015
[DOI]
Necessary and sufficient conditions for the asymptotic distributions of coherence of ultra-high dimensional random matrices
with
The Annals of Probability, 42, 623-648, 2014
[DOI] [arXiv] [slides]
A max-norm constrained minimization approach to 1-bit matrix completion
with
Journal of Machine Learning Research, 14, 3619-3647, 2013
[DOI]
with
Wiley StatsRef: Statistics Reference Online, 2018
[DOI] [pdf]
Self-normalization: Taming a wild population in a heavy-tailed world
with
Applied Mathematics-A Journal of Chinese Universities, 32, 253-269, 2017
[DOI]
College of Business Administration
University of Illinois at Chicago
601 S. Morgan St.
Chicago, IL 60607
Phone: (312) 355 0246
E-mail: wenxinz@uic.edu
Office: University Hall 2423
I am an Associate Professor in the Department of Information and Decision Sciences within the College of Business Administration at the University of Illinois Chicago [Google Scholar & CV].
My recent research interests center around the development and analysis of statistical methods (estimation and inference) and optimization tools for structured high-dimensional data problems, including sparse regression, low-rank, and nonparametric models. 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.
P.S. I don't respond to email requests for recommendation letters unless I know you well. If you were rarely in my class (e.g. MATH 189), it's clear that I don't know you well.
Complete Publications (arXiv)
Selected Publications
Robust estimation and inference for expected shortfall regression with many regressorswith
Journal of the Royal Statistical Society: Series B, 85(4): 1223–1246, 2023
[DOI] [preprint] [slides]
Smoothed quantile regression with large-scale inference
with
Journal of Econometrics, 232(2): 367-388, 2023
[DOI] [R package] [Python code] [slides]
Scalable estimation and inference for censored quantile regression process
with
The Annals of Statistics, 50(5): 2899-2924, 2022
[DOI] [supplement] [R code]
Communication-constrained distributed quantile regression with optimal statistical guarantees
with
Journal of Machine Learning Research, 23(272): 1-61, 2022
[DOI]
High-dimensional quantile regression: convolution smoothing and concave regularization
with
Journal of the Royal Statistical Society: Series B, 84(1): 205-233, 2022
[DOI] [supplement] [R package] [Python code]
Multiplier bootstrap for quantile regression: Non-asymptotic theory under random design
with
Information and Inference: A Journal of the IMA, 10, 813-861, 2021
[DOI] [R code]
A new principle for tuning-free Huber regression
with
Statistica Sinica, 31, 2153-2177, 2021
[DOI] [supplement] [R package]
Iteratively reweighted l1-penalized robust regression
with
Electronic Journal of Statistics, 15, 3287-3348, 2021
[DOI] [R package] [Python code]
Robust inference via multiplier bootstrap
with
The Annals of Statistics, 48, 1665-1691, 2020
[DOI] [supplement] [Matlab code]
Adaptive Huber regression
with
Journal of the American Statistical Association, 115, 254-265, 2020
[DOI] [arXiv] [R package] [slides]
FarmTest: Factor-adjusted robust multiple testing with approximate false discovery control
with
Journal of the American Statistical Association, 114, 1880-1893, 2019
[DOI] [R package]
User-friendly covariance estimation for heavy-tailed distributions
with
Statistical Science, 34, 454-471, 2019
[DOI]
A new perspective on robust M-estimation: Finite sample theory and applications to dependence-adjusted multiple testing
with
The Annals of Statistics, 46, 1904-1931, 2018
[DOI] [arXiv]
Are discoveries spurious? Distributions of maximum spurious correlations and their applications
with
The Annals of Statistics, 46, 989-1017, 2018
[DOI] [arXiv] [slides]
Max-norm optimization for robust matrix recovery
with
Mathematical Programming, Series B, 167, 5-35, 2018
[DOI] [arXiv]
Simulation-based hypothesis testing of high dimensional means under covariance heterogeneity
with
Biometrics, 73, 1300-1310, 2017
[DOI] [arXiv] [slides]
Comparing large covariance matrices under weak conditions on the dependence structure and its application to gene clustering
with
Biometrics, 73, 31-41, 2017
[DOI] [arXiv]
Guarding against spurious discoveries in high dimensions
with
Journal of Machine Learning Research, 17(203), 1-34, 2016
[DOI] [arXiv] [slides]
Nonparametric covariate-adjusted regression
with
The Annals of Statistics, 44, 2190-2220, 2016
[DOI]
Cramér-type moderate deviations for Studentized two-sample U-statistics with applications
with
The Annals of Statistics, 44, 1931-1956, 2016
[DOI] [slides]
Matrix completion via max-norm constrained optimization
with
Electronic Journal of Statistics, 10, 1493-1525, 2016
[DOI]
Cramér type moderate deviation theorems for self-normalized processes
with
Bernoulli, 22, 2029-2079, 2016
[DOI]
Nonparametric and parametric estimators of prevalence from group testing data with aggregated covariates
with
Journal of the American Statistical Association, 110, 1785-1796, 2015
[DOI]
Necessary and sufficient conditions for the asymptotic distributions of coherence of ultra-high dimensional random matrices
with
The Annals of Probability, 42, 623-648, 2014
[DOI] [arXiv] [slides]
A max-norm constrained minimization approach to 1-bit matrix completion
with
Journal of Machine Learning Research, 14, 3619-3647, 2013
[DOI]
Review Articles
Principal component analysis for big datawith
Wiley StatsRef: Statistics Reference Online, 2018
[DOI] [pdf]
Self-normalization: Taming a wild population in a heavy-tailed world
with
Applied Mathematics-A Journal of Chinese Universities, 32, 253-269, 2017
[DOI]
Editorial Service
01/2022-Present: Associate Editor, Annals of Statistics
01/2022-Present: Associate Editor, Annals of Applied Probability
01/2022-Present: Associate Editor, JRSSB
01/2020-08/2023: Associate Editor, Statistics: A Jnl of Theor. & Appl. Stat
Bio
2023-Present: Associate Professor, Department of Information and Decision Sciences, University of Illinois Chicago
2021-23: Associate Professor, Department of Mathematics, University of California, San Diego
2017-21: Assistant Professor, Department of Mathematics, University of California, San Diego
2015-17: Postdoctoral Research Associate, Department of Operations Research and Financial Engineering, Princeton University
2013-15: Research Fellow, School of Mathematics and Statistics, University of Melbourne