Some Modified Ridge Estimators for Handling the Multicollinearity Problem

Nusrat Shaheen, Ismail Shah (), Amani Almohaimeed (), Sajid Ali and Hana N. Alqifari
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Nusrat Shaheen: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Ismail Shah: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Amani Almohaimeed: Department of Statistics and Operation Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia
Sajid Ali: Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan
Hana N. Alqifari: Department of Statistics and Operation Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia

Mathematics, 2023, vol. 11, issue 11, 1-19

Abstract: Regression analysis is a statistical process that utilizes two or more predictor variables to predict a response variable. When the predictors included in the regression model are strongly correlated with each other, the problem of multicollinearity arises in the model. Due to this problem, the model variance increases significantly, leading to inconsistent ordinary least-squares estimators that may lead to invalid inferences. There are numerous existing strategies used to solve the multicollinearity issue, and one of the most used methods is ridge regression. The aim of this work is to develop novel estimators for the ridge parameter “ γ ” and compare them with existing estimators via extensive Monte Carlo simulation and real data sets based on the mean squared error criterion. The study findings indicate that the proposed estimators outperform the existing estimators.

Keywords: ridge regression; statistical model; multicollinearity; prediction; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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