Rank-based ridge estimation in multiple linear regression

DOI10.1080/10485252.2014.964714zbMath1329.62329OpenAlexW2038841532MaRDI QIDQ2934401

Asuman Turkmen, Omer Ozturk

Publication date: 12 December 2014

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2014.964714

zbMATH Keywords

rank regressionridge regressionrobust estimationshrinkagemulticollinearity

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Parametric hypothesis testing (62F03)

Related Items (1)

A semi-parametric approach to feature selection in high-dimensional linear regression models

Cites Work

• Rank-based variable selection
• Weighted Wilcoxon‐Type Smoothly Clipped Absolute Deviation Method
• Asymptotic normality ofr-estimates in the linear model
• Rank Methods for Combination of Independent Experiments in Analysis of Variance
• Augmented Robust Estimators
• ROBUST RIDGE REGRESSION BASED ON AN M-ESTIMATOR
• A biased-robust regression technique for the combined outlier-multicollinearity problem
• Estimating Regression Coefficients by Minimizing the Dispersion of the Residuals
• An Adaptive, Rate-Optimal Test of a Parametric Mean-Regression Model Against a Nonparametric Alternative
• Robust Nonparametric Statistical Methods
• Nonparametric Estimate of Regression Coefficients

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