Performance of some robust improved Kibria-Lukman M-estimators for linear regression model in presence of multicollinearity and outliers
- 13 January 2026
- journal article
- Published by Taylor & Francis in Communications in Statistics - Simulation and Computation
- p. 1-21
Abstract
No abstract availableThis publication has 18 references indexed in Scilit:
- Bagging-based heteroscedasticity-adjusted ridge estimators in the linear regression modelKuwait Journal of Science, 2025
- Improved Heteroscedasticity-Consistent Ridge Estimators for Linear Regression with MulticollinearityIranian Journal of Science, 2023
- Condition-index based new ridge regression estimator for linear regression model with multicollinearityKuwait Journal of Science, 2023
- Modified robust ridge M-estimators for linear regression models: an application to tobacco dataJournal of Statistical Computation and Simulation, 2023
- A robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliersConcurrency and Computation: Practice and Experience, 2022
- New robust ridge estimators for the linear regression model with outliersCommunications in Statistics - Simulation and Computation, 2021
- Quantile based estimation of biasing parameters in ridge regression modelCommunications in Statistics - Simulation and Computation, 2019
- A modified ridge m-estimator for linear regression model with multicollinearity and outliersCommunications in Statistics - Simulation and Computation, 2017
- Robust Statistics1981
- A Monte Carlo Evaluation of Some Ridge-Type EstimatorsJournal of the American Statistical Association, 1975