Influence diagnostics in gamma ridge regression model. Issue 3 (11th February 2019)

• Record Type: Journal Article
• Authors: Amin, Muhammad; Amanullah, Muhammad; Aslam, Muhammad; Qasim, Muhammad
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 3(2019)
• Page Start: 536
• Page End: 556
• Publication Date: 2019-02-11
• Subjects: GRM -- GRRM -- influential observation -- Pearson residuals -- multicollinearity -- ridge estimates; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2018.1558226
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In this article, we proposed some influence diagnostics for the gamma regression model (GRM) and the gamma ridge regression model (GRRM). We assess the impact of influential observations on the GRM and GRRM estimates by extending the work of Pregibon [Logistic regression diagnostics. Ann Stat. 1981;9:705–724] and Walker and Birch [Influence measures in ridge regression. Technometrics. 1988;30:221–227]. Comparison of both models is made and demonstrated with the help of a simulation study and a real data set. We report some momentous results in detecting the influential observations and their effects on the GRM and GRRM estimates.