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More on the Ridge Parameter Estimators for the Gamma Ridge Regression Model: Simulation and Applications
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
Jönköping University, Jönköping International Business School, JIBS, Statistics.ORCID iD: 0000-0003-0279-5305
Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya.
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2022 (English)In: Mathematical problems in engineering (Print), ISSN 1024-123X, E-ISSN 1563-5147, article id 6769421Article in journal (Refereed) Published
Abstract [en]

The Gamma ridge regression estimator (GRRE) is commonly used to solve the problem of multicollinearity, when the response variable follows the gamma distribution. Estimation of the ridge parameter estimator is an important issue in the GRRE as well as for other models. Numerous ridge parameter estimators are proposed for the linear and other regression models. So, in this study, we generalized these estimators for the Gamma ridge regression model. A Monte Carlo simulation study and two real-life applications are carried out to evaluate the performance of the proposed ridge regression estimators and then compared with the maximum likelihood method and some existing ridge regression estimators. Based on the simulation study and real-life applications results, we suggest some better choices of the ridge regression estimators for practitioners by applying the Gamma regression model with correlated explanatory variables. 

Place, publisher, year, edition, pages
Hindawi Publishing Corporation, 2022. article id 6769421
Keywords [en]
Intelligent systems, Maximum likelihood estimation, Monte Carlo methods, Parameter estimation, Model application, Modeling simulation, Monte Carlo's simulation, Multicollinearity, Parameters estimators, Real-life applications, Regression modelling, Ridge regression, Ridge regression estimators, Simulation studies, Regression analysis
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-56613DOI: 10.1155/2022/6769421ISI: 000797562900004Scopus ID: 2-s2.0-85130315012Local ID: GOA;intsam;814145OAI: oai:DiVA.org:hj-56613DiVA, id: diva2:1661730
Available from: 2022-05-30 Created: 2022-05-30 Last updated: 2022-05-30Bibliographically approved

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Qasim, Muhammad

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