A novel comparison of shrinkage methods based on multi criteria decision making in case of multicollinearity
From MaRDI portal
Publication:6593227
DOI10.3934/jimo.2024072MaRDI QIDQ6593227
Fatma Yerlikaya Özkurt, Sevval Kilicoglu
Publication date: 26 August 2024
Published in: Journal of Industrial and Management Optimization (Search for Journal in Brave)
ridge regressionmulticollinearityLassoshrinkage methodselastic netTOPSISmulti criteria decision making
Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Multi-objective and goal programming (90C29)
Cites Work
- Unnamed Item
- Unnamed Item
- Multicriteria decision methods: an attempt to evaluate and unify
- Enhanced ridge regressions
- Multiple attribute decision making. Methods and applications. A state-of- the-art survey
- Multiple Attribute Decision Making
- Combining Unbiased Ridge and Principal Component Regression Estimators
- Ridge Regression in Practice
- On the equivalence of operational performance measurement and multiple attribute decision making
- Performance of Some New Ridge Regression Estimators
- An Introduction to Statistical Learning
- Modified Ridge Regression Estimators
- A test of harmful multicollinearity: A generalized ridge regression approach
- Shrinkage parameter selection via modified cross-validation approach for ridge regression model
- Partial ridge regression under multicollinearity
- Dealing with big data: comparing dimension reduction and shrinkage regression methods
- A Generalized Stochastic Restricted Ridge Regression Estimator
- Regularization and Variable Selection Via the Elastic Net
- Shrinkage Ridge Estimators in Linear Regression
- Ridge Regression Estimation for Survey Samples
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Ridge Regression: Applications to Nonorthogonal Problems
- Comment: Ridge Regression—Still Inspiring After 50 Years
This page was built for publication: A novel comparison of shrinkage methods based on multi criteria decision making in case of multicollinearity