Another proposal about the new two-parameter estimator for linear regression model with correlated regressors

In this article, we present a new general class of biased estimators which includes some popular estimators as special cases and discuss its properties for multiple linear regression models when regressors are correlated. This proposal is based on some modification in the existing new two-parameter estimator. Performance of the proposed estimator is compared with many of the leading estimators, using the mean squared error matrix criterion, mitigating the adverse effects of multicollinearity. An extensive simulation study has been provided with a numerical example to illustrate the superiority of the proposed estimator.

Automated summary

In this article, a new general class of biased estimators is presented, which includes some popular estimators as special cases, and its properties for multiple linear regression models with correlated regressors are discussed. The performance of the proposed estimator is compared with many leading estimators using the mean squared error matrix criterion, mitigating the negative effects of multicollinearity. An extensive simulation study and numerical example are provided to illustrate the superiority of the proposed estimator.