56 citations · 337 across the 23 of their papers we have counts for
23 papers
Marginal Regression on Transient State Occupation Probabilities with Clustered Multistate Process Data
Wenxian Zhou, Giorgos Bakoyannis, Ying Zhang +1
Clustered multistate process data are commonly encountered in multicenter observational studies and clinical trials. A clinically important estimand with such data is the marginal…
How do noise tails impact on deep ReLU networks?
Jianqing Fan, Yihong Gu, Wen-Xin Zhou
This paper investigates the stability of deep ReLU neural networks for nonparametric regression under the assumption that the noise has only a finite p-th moment. We unveil how the…
Semiparametric Marginal Regression for Clustered Competing Risks Data with Missing Cause of Failure
Wenxian Zhou, Giorgos Bakoyannis, Ying Zhang +1
Clustered competing risks data are commonly encountered in multicenter studies. The analysis of such data is often complicated due to informative cluster size, a situation where th…
BEAUTY Powered BEAST
Kai Zhang, Wan Zhang, Zhigen Zhao +1
We study distribution-free goodness-of-fit tests with the proposed Binary Expansion Approximation of UniformiTY (BEAUTY) approach. This method generalizes the renowned Euler's form…
Smoothed Quantile Regression with Large-Scale Inference
Xuming He, Xiaoou Pan, Kean Ming Tan +1
Quantile regression is a powerful tool for learning the relationship between a response variable and a multivariate predictor while exploring heterogeneous effects. In this paper,…
On the Asymptotic Distribution of the Scan Statistic for Empirical Distributions
Andrew Ying, Wen-Xin Zhou
We investigate the asymptotic behavior of several variants of the scan statistic applied to empirical distributions, which can be applied to detect the presence of an anomalous int…
Iteratively Reweighted -Penalized Robust Regression
Xiaoou Pan, Qiang Sun, Wen-Xin Zhou
This paper investigates tradeoffs among optimization errors, statistical rates of convergence and the effect of heavy-tailed errors for high-dimensional robust regression with nonc…
Robust Inference via Multiplier Bootstrap
Xi Chen, Wen-Xin Zhou
This paper investigates the theoretical underpinnings of two fundamental statistical inference problems, the construction of confidence sets and large-scale simultaneous hypothesis…
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
Yuan Ke, Stanislav Minsker, Zhao Ren +2
We offer a survey of recent results on covariance estimation for heavy-tailed distributions. By unifying ideas scattered in the literature, we propose user-friendly methods that fa…
Principal component analysis for big data
Jianqing Fan, Qiang Sun, Wen-Xin Zhou +1
Big data is transforming our world, revolutionizing operations and analytics everywhere, from financial engineering to biomedical sciences. The complexity of big data often makes d…
A New Perspective on Robust -Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing
Wen-Xin Zhou, Koushiki Bose, Jianqing Fan +1
Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled by a single grossly outlying observation. As argued in the seminal work of Peter Huber i…
FarmTest: Factor-Adjusted Robust Multiple Testing with Approximate False Discovery Control
Jianqing Fan, Yuan Ke, Qiang Sun +1
Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for esti…
Max-Norm Optimization for Robust Matrix Recovery
Ethan X. Fang, Han Liu, Kim-Chuan Toh +1
This paper studies the matrix completion problem under arbitrary sampling schemes. We propose a new estimator incorporating both max-norm and nuclear-norm regularization, based on…
Testing for high-dimensional white noise using maximum cross-correlations
Jinyuan Chang, Qiwei Yao, Wen Zhou
We propose a new omnibus test for vector white noise using the maximum absolute auto-correlations and cross-correlations of the component series. Based on the newly established app…
On Gaussian Comparison Inequality and Its Application to Spectral Analysis of Large Random Matrices
Fang Han, Sheng Xu, Wen-Xin Zhou
Recently, Chernozhukov, Chetverikov, and Kato [Ann. Statist. 42 (2014) 1564--1597] developed a new Gaussian comparison inequality for approximating the suprema of empirical process…
Nonparametric covariate-adjusted regression
Aurore Delaigle, Peter Hall, Wen-Xin Zhou
We consider nonparametric estimation of a regression curve when the data are observed with multiplicative distortion which depends on an observed confounding variable. We suggest s…
Guarding against Spurious Discoveries in High Dimensions
Jianqing Fan, Wen-Xin Zhou
Many data mining and statistical machine learning algorithms have been developed to select a subset of covariates to associate with a response variable. Spurious discoveries can ea…
Two-Sample Smooth Tests for the Equality of Distributions
Wen-Xin Zhou, Chao Zheng, Zhen Zhang
This paper considers the problem of testing the equality of two unspecified distributions. The classical omnibus tests such as the Kolmogorov-Smirnov and Cramèr-von Mises are known…
Are Discoveries Spurious? Distributions of Maximum Spurious Correlations and Their Applications
Jianqing Fan, Qi-Man Shao, Wen-Xin Zhou
Over the last two decades, many exciting variable selection methods have been developed for finding a small group of covariates that are associated with the response from a large p…
Cramér-type moderate deviations for Studentized two-sample -statistics with applications
Jinyuan Chang, Qi-Man Shao, Wen-Xin Zhou
Two-sample -statistics are widely used in a broad range of applications, including those in the fields of biostatistics and econometrics. In this paper, we establish sharp Cramé…
Simulation-Based Hypothesis Testing of High Dimensional Means Under Covariance Heterogeneity
Jinyuan Chang, Chao Zheng, Wen-Xin Zhou +1
In this paper, we study the problem of testing the mean vectors of high dimensional data in both one-sample and two-sample cases. The proposed testing procedures employ maximum-typ…
Cramér type moderate deviation theorems for self-normalized processes
Qi-Man Shao, Wen-Xin Zhou
Cramér type moderate deviation theorems quantify the accuracy of the relative error of the normal approximation and provide theoretical justifications for many commonly used method…
Necessary and sufficient conditions for the asymptotic distributions of coherence of ultra-high dimensional random matrices
Qi-Man Shao, Wen-Xin Zhou
Let be a random sample from a -dimensional population distribution, where and for some , and let…