An iterative approach to minimize the mean squared error in ridge regression

DOI10.1007/s00180-015-0557-yzbMath1317.65047OpenAlexW2043495477MaRDI QIDQ2354752

Yanyan Li

Publication date: 24 July 2015

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-015-0557-y

zbMATH Keywords

least squaresmulticollinearityoptimal ridge parameter

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (6)

Robust ridge M-estimators with pretest and Stein-rule shrinkage for an intercept term ⋮ Bootstrap selection of ridge regularization parameter: a comparative study via a simulation study ⋮ Optimal determination of the parameters of some biased estimators using genetic algorithm ⋮ Comparison of deviance and ridge deviance residual-based control charts for monitoring Poisson profiles ⋮ A Bayesian approach with generalized ridge estimation for high-dimensional regression and testing ⋮ Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Linear regression.
• Some Modifications for Choosing Ridge Parameters
• A Monte Carlo Study of Recent Ridge Parameters
• On the almost unbiased ridge regression estimator
• On Some Ridge Regression Estimators: An Empirical Comparisons
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Ridge regression:some simulations
• A Monte Carlo Evaluation of Some Ridge-Type Estimators
• Ridge regression iterative estimation of the biasing parameter
• A simulation study of ridge and other regression estimators
• A Class of Biased Estimators in Linear Regression
• A Simulation Study of Alternatives to Ordinary Least Squares
• Regression
• Choosing Ridge Parameter for Regression Problems
• Performance of Some New Ridge Regression Estimators
• The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
• Ridge Regression – A Simulation Study
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Ridge Regression: Applications to Nonorthogonal Problems

This page was built for publication: An iterative approach to minimize the mean squared error in ridge regression