A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model
Open Access
Abstract
In a multiple linear regression model, the ordinary least squares estimator is inefficient when the multicollinearity problem exists. Many authors have proposed different estimators to overcome the multicollinearity problem for linear regression models. This paper introduces a new regression estimator, called the Dawoud–Kibria estimator, as an alternative to the ordinary least squares estimator. Theory and simulation results show that this estimator performs better than other regression estimators under some conditions, according to the mean squares error criterion. The real-life datasets are used to illustrate the findings of the paper.Keywords
This publication has 28 references indexed in Scilit:
- Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterionComputational Statistics & Data Analysis, 2018
- A New Two-Parameter Estimator in Linear RegressionCommunications in Statistics - Theory and Methods, 2010
- The Restricted and Unrestricted Two-Parameter EstimatorsCommunications in Statistics - Theory and Methods, 2007
- A new biased estimator based on ridge estimationStatistical Papers, 2006
- On the almost unbiased generalized liu estimator and unbiased estimation of the bias and mseCommunications in Statistics - Theory and Methods, 1995
- A new class of blased estimate in linear regressionCommunications in Statistics - Theory and Methods, 1993
- Good ridge estimators based on prior informationCommunications in Statistics - Theory and Methods, 1976
- On Biased Estimation in Linear ModelsTechnometrics, 1973
- Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 1970
- Principal Components Regression in Exploratory Statistical ResearchJournal of the American Statistical Association, 1965