Quantile regression feature selection and estimation with grouped variables using Huber approximation
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Publication:2080351
DOI10.1007/s11222-022-10135-wzbMath1496.62022OpenAlexW4295136015WikidataQ114223419 ScholiaQ114223419MaRDI QIDQ2080351
Publication date: 7 October 2022
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11222-022-10135-w
Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Uses Software
Cites Work
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- Asymmetric Least Squares Estimation and Testing
- Regularization of case-specific parameters for robustness and efficiency
- Finite mixtures of quantile and M-quantile regression models
- Oracle inequalities and optimal inference under group sparsity
- Simultaneous estimation and variable selection in median regression using Lasso-type penalty
- The MM alternative to EM
- The benefit of group sparsity
- Variable selection in nonparametric additive models
- On parameters of increasing dimensions
- 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
- A new perspective on robust \(M\)-estimation: finite sample theory and applications to dependence-adjusted multiple testing
- Robust regression: Asymptotics, conjectures and Monte Carlo
- Simultaneous analysis of Lasso and Dantzig selector
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Adaptive group Lasso selection in quantile models
- Adaptive Huber Regression
- Asymptotic Properties and Variance Estimators of the M-quantile Regression Coefficients Estimators
- NonparametricM-quantile regression using penalised splines
- The Group Lasso for Logistic Regression
- M-quantiles
- Regression Quantiles
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension
- Quantile regression via iterative least squares computations
- Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules
- Estimation of High Dimensional Mean Regression in the Absence of Symmetry and Light Tail Assumptions
- Model Selection and Estimation in Regression with Grouped Variables
- M-quantile models for small area estimation
- Robust Estimation of a Location Parameter
- Strong Rules for Discarding Predictors in Lasso-Type Problems
- Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors
- A fast unified algorithm for solving group-lasso penalize learning problems
- A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers
- Fast Calibrated Additive Quantile Regression
- Estimation and Testing in M‐quantile Regression with Applications to Small Area Estimation
