Cross-Fitted Residual Regression for High-Dimensional Heteroscedasticity Pursuit

DOI10.1080/01621459.2021.1970570OpenAlexW3193493756MaRDI QIDQ6165292

Hui Zou, Le Zhou

Publication date: 4 July 2023

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://figshare.com/articles/journal_contribution/Cross-fitted_Residual_Regression_for_High_Dimensional_Heteroscedasticity_Pursuit/15832137

zbMATH Keywords

heteroscedasticitysparsityhigh dimensionmodel selection criterionHBIC

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Diagnostics for heteroscedasticity in regression
• Nearly unbiased variable selection under minimax concave penalty
• Asymmetric Least Squares Estimation and Testing
• High-dimensional generalizations of asymmetric least squares regression and their applications
• Statistics for high-dimensional data. Methods, theory and applications.
• One-step sparse estimates in nonconcave penalized likelihood models
• Relaxed Lasso
• High-dimensional additive modeling
• Least angle regression. (With discussion)
• Variable selection in linear mixed effects models
• Simultaneous analysis of Lasso and Dantzig selector
• Calibrating nonconvex penalized regression in ultra-high dimension
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Strong oracle optimality of folded concave penalized estimation
• The fused Kolmogorov filter: a nonparametric model-free screening method
• High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression
• Error Variance Estimation in Ultrahigh-Dimensional Additive Models
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• Statistical Foundations of Data Science
• Estimation And Selection Via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications
• Tuning Parameter Selection in High Dimensional Penalized Likelihood
• A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers