18 citations · 28 across the 8 of their papers we have counts for
8 papers
Deep neural expected shortfall regression with tail-robustness
Myeonghun Yu, Kean Ming Tan, Huixia Judy Wang +1
Expected shortfall (ES), also known as conditional value-at-risk, is a widely recognized risk measure that complements value-at-risk by capturing tail-related risks more effectivel…
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…
Estimation of Complier Expected Shortfall Treatment Effects with a Binary Instrumental Variable
Bo Wei, Kean Ming Tan, Xuming He
Estimating the causal effect of a treatment or exposure for a subpopulation is of great interest in many biomedical and economical studies. Expected shortfall, also referred to as…
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…
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…
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,…