18 citations · 45 across the 16 of their papers we have counts for
13 papers · 1 filter
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…
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…