Variable selection and debiased estimation for single‐index expectile model

DOI10.1111/anzs.12348zbMath1521.62052OpenAlexW4210513476MaRDI QIDQ6075136

Yexun Peng, Unnamed Author, Rong Jiang

Publication date: 20 October 2023

Published in: Australian & New Zealand Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/anzs.12348

zbMATH Keywords

variable selectionsingle-index modelexpectile regressiondebiasing technique

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric tolerance and confidence regions (62G15)

Cites Work

• On asymptotically optimal confidence regions and tests for high-dimensional models
• Single-index quantile regression
• The Adaptive Lasso and Its Oracle Properties
• Asymmetric Least Squares Estimation and Testing
• Confidence intervals for high-dimensional partially linear single-index models
• Assessing value at risk with CARE, the conditional autoregressive expectile models
• High-dimensional generalizations of asymmetric least squares regression and their applications
• Expectiles and \(M\)-quantiles are quantiles
• Expectile regression for analyzing heteroscedasticity in high dimension
• Semi-parametric estimation of partially linear single-index models
• COHERENCE AND ELICITABILITY
• Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
• M-quantiles
• Variable selection in expectile regression
• Estimation and variable selection in single-index composite quantile regression
• An Adaptive Estimation of Dimension Reduction Space
• Identifiability of single-index models and additive-index models
• Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models

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