Bootstrap confidence interval of ridge regression in linear regression model: A comparative study via a simulation study
DOI10.1080/03610926.2022.2045024OpenAlexW4214869764MaRDI QIDQ6096199
Unnamed Author, M. Revan Özkale
Publication date: 11 September 2023
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2022.2045024
bootstrapconfidence intervalsridge regressioncoverage probabilityregularization parametersignal to noise ratio
Ridge regression; shrinkage estimators (Lasso) (62J07) Bootstrap, jackknife and other resampling methods (62F40) Pseudo-random numbers; Monte Carlo methods (11K45)
Cites Work
- Asymptotic confidence intervals in ridge regression based on the Edgeworth expansion
- Bootstrap methods: another look at the jackknife
- Some Modifications for Choosing Ridge Parameters
- A Monte Carlo Study of Recent Ridge Parameters
- 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
- A simulation study of ridge and other regression estimators
- A Class of Biased Estimators in Linear Regression
- Classical F-Tests and Confidence Regions for Ridge 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: Biased Estimation for Nonorthogonal Problems
This page was built for publication: Bootstrap confidence interval of ridge regression in linear regression model: A comparative study via a simulation study
