Retire: robust expectile regression in high dimensions

DOI10.1016/j.jeconom.2023.04.004arXiv2212.05562MaRDI QIDQ6150528

Wen-Xin Zhou, Rebeka Man, Kean Ming Tan, Zi-An Wang

Publication date: 6 March 2024

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2212.05562

zbMATH Keywords

robustnessquantile regressionconcave regularizationexpectile regressionheavy-tailed error

Mathematics Subject Classification ID

Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Making and Evaluating Point Forecasts
• On asymptotically optimal confidence regions and tests for high-dimensional models
• Nearly unbiased variable selection under minimax concave penalty
• Asymmetric Least Squares Estimation and Testing
• Assessing value at risk with CARE, the conditional autoregressive expectile models
• Some new asymptotic theory for least squares series: pointwise and uniform results
• High-dimensional generalizations of asymmetric least squares regression and their applications
• A general theory of hypothesis tests and confidence regions for sparse high dimensional models
• Statistics for high-dimensional data. Methods, theory and applications.
• Additive models for quantile regression: model selection and confidence bands
• One-step sparse estimates in nonconcave penalized likelihood models
• Expectiles and \(M\)-quantiles are quantiles
• The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. With comments by Ronald A. Thisted and M. R. Osborne and a rejoinder by the authors
• False discoveries occur early on the Lasso path
• I-LAMM for sparse learning: simultaneous control of algorithmic complexity and statistical error
• Slope meets Lasso: improved oracle bounds and optimality
• Statistical consistency and asymptotic normality for high-dimensional robust \(M\)-estimators
• Necessary and sufficient conditions for variable selection consistency of the Lasso in high dimensions
• Iteratively reweighted \(\ell_1\)-penalized robust regression
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Strong oracle optimality of folded concave penalized estimation
• COHERENCE AND ELICITABILITY
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• Adaptive Huber Regression
• One-Step Huber Estimates in the Linear Model
• On the Estimation of Production Frontiers: Maximum Likelihood Estimation of the Parameters of a Discontinuous Density Function
• Regression Quantiles
• Numerical Optimization
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Estimation of Tail Risk Based on Extreme Expectiles
• High-Dimensional Statistics
• On elicitable risk measures
• Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
• Statistical Foundations of Data Science
• Aggregated Expectile Regression by Exponential Weighting
• Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models
• Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
• Instrumental Variables Estimates of the Effect of Subsidized Training on the Quantiles of Trainee Earnings
• Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
• Robust Estimation of a Location Parameter
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models
• A general theory of concave regularization for high-dimensional sparse estimation problems

This page was built for publication: Retire: robust expectile regression in high dimensions