2 citations · 5 across the 4 of their papers we have counts for
4 papers
Double/Debiased Machine Learning for Logistic Partially Linear Model
Molei Liu, Yi Zhang, Doudou Zhou
We propose double/debiased machine learning approaches to infer (at the parametric rate) the parametric component of a logistic partially linear model with the binary response foll…
Robust Estimation and Shrinkage in Ultrahigh Dimensional Expectile Regression with Heavy Tails and Variance Heterogeneity
Jun Zhao, Guan'ao Yan, Yi Zhang
High-dimensional data subject to heavy-tailed phenomena and heterogeneity are commonly encountered in various scientific fields and bring new challenges to the classical statistica…
Semiparametric Expectile Regression for High-dimensional Heavy-tailed and Heterogeneous Data
Jun Zhao, Guan'ao Yan, Yi Zhang
Recently, high-dimensional heterogeneous data have attracted a lot of attention and discussion. Under heterogeneity, semiparametric regression is a popular choice to model data in…
Conditional Tail-Related Risk Estimation Using Composite Asymmetric Least Squares and Empirical Likelihood
Sheng Wu, Yi Zhang, Jun Zhao +1
In this article, by using composite asymmetric least squares (CALS) and empirical likelihood, we propose a two-step procedure to estimate the conditional value at risk (VaR) and co…