5 citations · 7 across the 7 of their papers we have counts for
8 papers
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…
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…
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…
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…