Marginal maximum likelihood estimation methods for the tuning parameters of ridge, power ridge, and generalized ridge regression

DOI10.1080/03610918.2017.1321119OpenAlexW2609849720MaRDI QIDQ5084943

George Karabatsos

Publication date: 29 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2017.1321119

zbMATH Keywords

Bayesian analysisridge regressionLASSOelastic net

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (2)

Estimation of variance components, heritability and the ridge penalty in high-dimensional generalized linear models ⋮ Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Uses Software

• glmnet
• ElemStatLearn
• Bayesian Regression

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Some priors for sparse regression modelling
• Estimating the dimension of a model
• Least angle regression. (With discussion)
• The Bayesian elastic net
• A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs
• Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
• Ridge regression:some simulations
• A simulation study of ridge and other regression estimators
• 10.1162/15324430152748236
• A Statistical View of Some Chemometrics Regression Tools
• Regularization Parameter Selections via Generalized Information Criterion
• Linear Model Selection by Cross-Validation
• Regularization and Variable Selection Via the Elastic Net
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Local Shrinkage Rules, Lévy Processes and Regularized Regression
• Tuning Parameter Selection in High Dimensional Penalized Likelihood

This page was built for publication: Marginal maximum likelihood estimation methods for the tuning parameters of ridge, power ridge, and generalized ridge regression