A new Liu-type estimator for the Inverse Gaussian Regression Model. Issue 7 (2nd May 2020)

• Record Type: Journal Article
• Authors: Akram, Muhammad Nauman; Amin, Muhammad; Qasim, Muhammad
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 7(2020)
• Page Start: 1153
• Page End: 1172
• Publication Date: 2020-05-02
• Subjects: Inverse Gaussian Regression Model -- multicollinearity -- maximum likelihood estimator -- Liu-type estimator -- mean squared error -- application of IGRM -- GDP -- IGRRE -- IGLE -- IGLTE; 62J07 -- 62J12; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1718150
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: The Inverse Gaussian Regression Model (IGRM) is used when the response variable is positively skewed and follows the inverse Gaussian distribution. In this article, we propose a Liu-type estimator to combat multicollinearity in the IGRM. The variance of the Maximum Likelihood Estimator (MLE) is overstated due to the presence of severe multicollinearity. Moreover, some estimation methods are suggested to estimate the optimal value of the shrinkage parameter. The performance of the proposed estimator is compared with the MLE and some other existing estimators in the sense of mean squared error through Monte Carlo simulation and different real-life applications. Under certain conditions, it is concluded that the proposed estimator is superior to the MLE, ridge, and Liu estimator.