no code implementations • 11 Dec 2022 • Rebeka Man, Kean Ming Tan, Zian Wang, Wen-Xin Zhou
In this paper, we propose and study (penalized) robust expectile regression (retire), with a focus on iteratively reweighted $\ell_1$-penalization which reduces the estimation bias from $\ell_1$-penalization and leads to oracle properties.
no code implementations • 20 Mar 2022 • 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.
no code implementations • 25 Oct 2021 • Kean Ming Tan, Heather Battey, Wen-Xin Zhou
We address the problem of how to achieve optimal inference in distributed quantile regression without stringent scaling conditions.
1 code implementation • 9 Dec 2020 • Xuming He, Xiaoou Pan, Kean Ming Tan, Wen-Xin Zhou
Our numerical studies confirm the conquer estimator as a practical and reliable approach to large-scale inference for quantile regression.
Statistics Theory Methodology Statistics Theory
2 code implementations • 9 Jul 2019 • 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 nonconvex regularization.
1 code implementation • 5 Nov 2018 • Yuan Ke, Stanislav Minsker, Zhao Ren, Qiang Sun, Wen-Xin Zhou
We offer a survey of recent results on covariance estimation for heavy-tailed distributions.
Methodology Statistics Theory Statistics Theory
no code implementations • 24 Sep 2016 • Ethan X. Fang, Han Liu, Kim-Chuan Toh, Wen-Xin Zhou
This paper studies the matrix completion problem under arbitrary sampling schemes.
no code implementations • 24 Sep 2013 • T. Tony Cai, Wen-Xin Zhou
The rate of convergence for the estimate is obtained.
no code implementations • 2 Mar 2013 • T. Tony Cai, Wen-Xin Zhou
Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting.