High-dimensional robust approximated \(M\)-estimators for mean regression with asymmetric data
From MaRDI portal
Publication:2079618
DOI10.1016/j.jmva.2022.105080OpenAlexW2980862794WikidataQ113870423 ScholiaQ113870423MaRDI QIDQ2079618
Publication date: 30 September 2022
Published in: Journal of Multivariate Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1910.09493
Asymptotic properties of parametric estimators (62F12) Robustness and adaptive procedures (parametric inference) (62F35)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Nearly unbiased variable selection under minimax concave penalty
- Gradient methods for minimizing composite functions
- The \(L_1\) penalized LAD estimator for high dimensional linear regression
- Support recovery without incoherence: a case for nonconvex regularization
- A shrinkage principle for heavy-tailed data: high-dimensional robust low-rank matrix recovery
- Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
- Redescending \(M\)-estimators
- On parameters of increasing dimensions
- Robust regression through the Huber's criterion and adaptive lasso penalty
- Statistical consistency and asymptotic normality for high-dimensional robust \(M\)-estimators
- Iteratively reweighted \(\ell_1\)-penalized robust regression
- Scale calibration for high-dimensional robust regression
- High-Dimensional Heteroscedastic Regression with an Application to eQTL Data Analysis
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
- Robust Estimation of a Location Parameter
This page was built for publication: High-dimensional robust approximated \(M\)-estimators for mean regression with asymmetric data