Penalized MM regression estimation withLγpenalty: a robust version of bridge regression

DOI10.1080/02331888.2016.1159682zbMath1357.62233arXiv1511.08029OpenAlexW2290475781MaRDI QIDQ2953971

Olcay Arslan

Publication date: 11 January 2017

Published in: Unnamed Author (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.08029

zbMATH Keywords

ridge regressionrobust regressionvariable selectionpenalized regressionLassobridge regressionMM estimator

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (3)

Robust penalized empirical likelihood estimation method for linear regression ⋮ Robust regression estimation and variable selection when cellwise and casewise outliers are present ⋮ Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution

Uses Software

• R

Cites Work

• Weighted LAD-LASSO method for robust parameter estimation and variable selection in regression
• Robust and sparse bridge regression
• Selection of tuning parameters in bridge regression models via Bayesian information criterion
• High breakdown-point and high efficiency robust estimates for regression
• Simultaneous estimation and variable selection in median regression using Lasso-type penalty
• One-step sparse estimates in nonconcave penalized likelihood models
• Support vector machines with adaptive \(L_q\) penalty
• Asymptotics for Lasso-type estimators.
• Sparse least trimmed squares regression for analyzing high-dimensional large data sets
• Bridge regression: adaptivity and group selection
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• Correcting MM estimates for ``fat data sets
• Variable selection using MM algorithms
• Outlier Detection Using Nonconvex Penalized Regression
• LASSO-TYPE GMM ESTIMATOR
• Unified LASSO Estimation by Least Squares Approximation
• Regularization of Wavelet Approximations
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• Robust Statistics

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