A restricted gamma ridge regression estimator combining the gamma ridge regression and the restricted maximum likelihood methods of estimation. Issue 8 (24th May 2022)

• Record Type: Journal Article
• Authors: Qasim, Muhammad; Akram, Muhammad Nauman; Amin, Muhammad; Månsson, Kristofer
• Is Part Of: Journal of statistical computation and simulation. Volume 92:Issue 8(2022)
• Page Start: 1696
• Page End: 1713
• Publication Date: 2022-05-24
• Subjects: Gamma regression model -- maximum likelihood estimator -- multicollinearity -- mean squared error -- restricted gamma ridge regression estimator; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2021.2005063
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : In this article, we propose a restricted gamma ridge regression estimator (RGRRE) by combining the gamma ridge regression (GRR) and restricted maximum likelihood estimator (RMLE) to combat multicollinearity problem for estimating the parameter β in the gamma regression model. The properties of the new estimator are discussed, and its superiority over the GRR, RMLE and traditional maximum likelihood estimator is theoretically analysed under different conditions. We also suggest some estimating methods to find the optimal value of the shrinkage parameter. A Monte Carlo simulation study is conducted to judge the performance of the proposed estimator. Finally, an empirical application is analysed to show the benefit of RGRRE over the existing estimators.