As a result of exiting high-dimensional ill-posed results, more attention has been provided to regularisation methods in the last two decades. To obtain more meaningful predictors, a smaller subset of large numbers of predictors is required. A new way of incorporating the penalised concept in regularised regression is proposed in this paper. The proposed penalty is based on using the variances of the regression parameters from the least square estimator. Some penalised estimators such as ridge, lasso, and elastic net, which are used to resolve both the problem of multicollinearity and to select variables, are added to the proposed system. Using the average mean squared error criterion (AMSE), good results are achieved. For simulated details, the best results in the simulated data are also seen in real data. The type of the resulting estimators’ lower average prediction errors (APE).

**Author (s) Details**

**Magda M. M. Haggag
**Department of Statistics, Mathematics and Insurance, Faculty of Commerce, Damanhour University, Egypt.

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