On the performance of some new Liu parameters for the gamma regression model. Issue 16 (2nd November 2018)

• Record Type: Journal Article
• Authors: Qasim, Muhammad; Amin, Muhammad; Amanullah, Muhammad
• Is Part Of: Journal of statistical computation and simulation. Volume 88:Issue 16(2018)
• Page Start: 3065
• Page End: 3080
• Publication Date: 2018-11-02
• Subjects: Gamma regression -- maximum likelihood -- multicollinearity -- Liu estimator -- Liu parameter; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2018.1498502
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract: The maximum likelihood (ML) method is used to estimate the unknown Gamma regression (GR) coefficients. In the presence of multicollinearity, the variance of the ML method becomes overstated and the inference based on the ML method may not be trustworthy. To combat multicollinearity, the Liu estimator has been used. In this estimator, estimation of the Liu parameter d is an important problem. A few estimation methods are available in the literature for estimating such a parameter. This study has considered some of these methods and also proposed some new methods for estimation of the d . The Monte Carlo simulation study has been conducted to assess the performance of the proposed methods where the mean squared error (MSE) is considered as a performance criterion. Based on the Monte Carlo simulation and application results, it is shown that the Liu estimator is always superior to the ML and recommendation about which best Liu parameter should be used in the Liu estimator for the GR model is given.