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Performance of Asar and Genç and Huang and Yang’s Two-Parameter Estimation Methods for the Gamma Regression Model
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
Jönköping University, Jönköping International Business School, JIBS, Statistics. Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
2019 (English)In: Iranian Journal of Science and Technology, Transactions A: Science, ISSN 1028-6276, Vol. 43, no 6, p. 2951-2963Article in journal (Refereed) Published
Abstract [en]

This study assesses the performance of two-parameter estimation methods to combat multicollinearity in the Gamma regression model. We derived optimal values for two-parameter estimation methods in the Gamma regression model. Furthermore, we proposed some estimation methods to estimate the shrinkage parameters and these methods improve the efficiency of the two-parameter estimator. We compare the performance of these estimators by means of Monte Carlo simulation study where the mean squared error (MSE) is considered as a performance criterion. Finally, consider a reaction rate data to evaluate the performance of the estimators. The simulation and numerical example results showed that the two-parameter biased estimators have smaller MSE than the maximum likelihood estimator under certain conditions.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 43, no 6, p. 2951-2963
Keywords [en]
Gamma regression model, ML estimator, Mean squared error, Multicollinearity, Two-parameter estimator
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-46446DOI: 10.1007/s40995-019-00777-3Scopus ID: 2-s2.0-85073997368Local ID: ;IHHÖvrigtISOAI: oai:DiVA.org:hj-46446DiVA, id: diva2:1357319
Available from: 2019-10-03 Created: 2019-10-03 Last updated: 2020-01-14Bibliographically approved

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Qasim, Muhammad

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