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Influence diagnostics in gamma ridge regression model
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
2019 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 89, no 3, p. 536-556Article in journal (Refereed) Published
Abstract [en]

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. 

Place, publisher, year, edition, pages
Taylor & Francis, 2019. Vol. 89, no 3, p. 536-556
Keywords [en]
GRM, GRRM, influential observation, multicollinearity, Pearson residuals, ridge estimates
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-46439DOI: 10.1080/00949655.2018.1558226Scopus ID: 2-s2.0-85058682454OAI: oai:DiVA.org:hj-46439DiVA, id: diva2:1357287
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2019-10-03Bibliographically approved

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Qasim, Muhammad

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