New quantile based ridge M-estimator for linear regression models with multicollinearity and outliers
From MaRDI portal
Publication:6171863
DOI10.1080/03610918.2021.1884715OpenAlexW3131901145MaRDI QIDQ6171863
Sohail Chand, Muhammad Suhail, Muhammad Aslam
Publication date: 18 July 2023
Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610918.2021.1884715
outliersmean squared errorridge regressionsignal-to-noise ratiomulticollinearityM-estimatorridge parameter
Related Items
Cites Work
- Unnamed Item
- Mean Square Error Comparisons of the Alternative Estimators For The Distributed Lag Models
- Some Modifications for Choosing Ridge Parameters
- A Monte Carlo Study of Recent Ridge Parameters
- ROBUST RIDGE REGRESSION BASED ON AN M-ESTIMATOR
- A Monte Carlo Evaluation of Some Ridge-Type Estimators
- REVIEW AND CLASSIFICATIONS OF THE RIDGE PARAMETER ESTIMATION TECHNIQUES
- Choosing Ridge Parameter for Regression Problems
- Performance of Some New Ridge Regression Estimators
- Modified Ridge Regression Estimators
- Robust Liu-type estimator for regression based on M-estimator
- A comparison of some new and old robust ridge regression estimators
- Quantile-based robust ridge m-estimator for linear regression model in presence of multicollinearity and outliers
- Quantile based estimation of biasing parameters in ridge regression model
- A modified ridge m-estimator for linear regression model with multicollinearity and outliers
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Robust Statistics