18 citations · 45 across the 16 of their papers we have counts for
17 papers
Temporal-Difference Calibration of Black-Box Simulators
Min Zeng, Xin Wen, Xi Chen +2
We develop a derivative-free temporal-difference (TD) framework for calibrating black-box simulators using one-step transitions from a target system and noisy simulator-generated c…
Generalized Rank Regression
Jiyuan Tu, Suqi Wu, Yichen Zhang +1
Rank regression offers robustness to outliers and heavy-tailed response distributions, invariance to monotonic transformations, and improved efficiency under non-Gaussian errors, m…
Adapting to noise tails in private linear regression
Jinyuan Chang, Lin Yang, Mengyue Zha +1
While the traditional goal of statistics is to infer population parameters, modern practice increasingly demands protection of individual privacy. One way to address this need is t…
High-Dimensional Expected Shortfall Regression
Shushu Zhang, Xuming He, Kean Ming Tan +1
Expected shortfall is defined as the average over the tail below (or above) a certain quantile of a probability distribution. Expected shortfall regression provides powerful tools…
An Efficient Iterative Least Squares Algorithm for Large-dimensional Matrix Factor Model via Random Projection
Yong He, Ran Zhao, Wen-Xin Zhou
The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In this pa…
Robust Estimation and Inference for Expected Shortfall Regression with Many Regressors
Xuming He, Kean Ming Tan, Wen-Xin Zhou
Expected Shortfall (ES), also known as superquantile or Conditional Value-at-Risk, has been recognized as an important measure in risk analysis and stochastic optimization, and is…
Retire: Robust Expectile Regression in High Dimensions
Rebeka Man, Kean Ming Tan, Zian Wang +1
High-dimensional data can often display heterogeneity due to heteroscedastic variance or inhomogeneous covariate effects. Penalized quantile and expectile regression methods offer…
Scalable estimation and inference for censored quantile regression process
Xuming He, Xiaoou Pan, Kean Ming Tan +1
Censored quantile regression (CQR) has become a valuable tool to study the heterogeneous association between a possibly censored outcome and a set of covariates, yet computation an…
High-Dimensional Composite Quantile Regression: Optimal Statistical Guarantees and Fast Algorithms
Haeseong Moon, Wen-Xin Zhou
The composite quantile regression (CQR) was introduced by Zou and Yuan [Ann. Statist. 36 (2008) 1108--1126] as a robust regression method for linear models with heavy-tailed errors…
A Unified Algorithm for Penalized Convolution Smoothed Quantile Regression
Rebeka Man, Xiaoou Pan, Kean Ming Tan +1
Penalized quantile regression (QR) is widely used for studying the relationship between a response variable and a set of predictors under data heterogeneity in high-dimensional set…
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold Regression
Yinan Lin, Wen Zhou, Zhi Geng +2
In traditional logistic regression models, the link function is often assumed to be linear and continuous in predictors. Here, we consider a threshold model that all continuous fea…
Communication-Constrained Distributed Quantile Regression with Optimal Statistical Guarantees
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. This is challenging due to the non-smooth nature…
High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization
Kean Ming Tan, Lan Wang, Wen-Xin Zhou
-penalized quantile regression is widely used for analyzing high-dimensional data with heterogeneity. It is now recognized that the -penalty introduces non-negligib…
Distributed Adaptive Huber Regression
Jiyu Luo, Qiang Sun, Wenxin Zhou
Distributed data naturally arise in scenarios involving multiple sources of observations, each stored at a different location. Directly pooling all the data together is often prohi…
Adaptive Capped Least Squares
Qiang Sun, Rui Mao, Wen-Xin Zhou
This paper proposes the capped least squares regression with an adaptive resistance parameter, hence the name, adaptive capped least squares regression. The key observation is, by…
A Unified Framework for Testing High Dimensional Parameters: A Data-Adaptive Approach
Cheng Zhou, Xinsheng Zhang, Wenxin Zhou +1
High dimensional hypothesis test deals with models in which the number of parameters is significantly larger than the sample size. Existing literature develops a variety of individ…
Adaptive Huber Regression
Qiang Sun, Wenxin Zhou, Jianqing Fan
Big data can easily be contaminated by outliers or contain variables with heavy-tailed distributions, which makes many conventional methods inadequate. To address this challenge, w…