On the Performance of the Jackknifed Modified Ridge Estimator in the Linear Regression Model with Correlated or Heteroscedastic Errors

In this article, we introduce a new estimator by jackknifing the modified ridge estimator based on prior information for the vector of parameters in the linear regression model with correlated or heteroscedastic errors. Furthermore, we discuss its properties and compare that our new estimator is superior, in the mean square error matrix and mean square error sense, to the generalized least squares estimate, the generalized modified ridge estimator and the generalized jackknifed ridge estimator, respectively. Finally, a numerical example is done to illustrate some of the theoretical results.