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On the performance of some new Liu parameters for the gamma regression model
Department of Statistics and Computer Science, University of Veterinary and Animal Sciences, Lahore, Pakistan.
Department of Statistics, University of Sargodha, Sargodha, Pakistan.
Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan.
2018 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 88, no 16, p. 3065-3080Article in journal (Refereed) Published
Abstract [en]

The maximum likelihood (ML) method is used to estimate the unknown Gamma regression (GR) coefficients. In the presence of multicollinearity, the variance of the ML method becomes overstated and the inference based on the ML method may not be trustworthy. To combat multicollinearity, the Liu estimator has been used. In this estimator, estimation of the Liu parameter d is an important problem. A few estimation methods are available in the literature for estimating such a parameter. This study has considered some of these methods and also proposed some new methods for estimation of the d. The Monte Carlo simulation study has been conducted to assess the performance of the proposed methods where the mean squared error (MSE) is considered as a performance criterion. Based on the Monte Carlo simulation and application results, it is shown that the Liu estimator is always superior to the ML and recommendation about which best Liu parameter should be used in the Liu estimator for the GR model is given. 

Place, publisher, year, edition, pages
Taylor & Francis, 2018. Vol. 88, no 16, p. 3065-3080
Keywords [en]
Gamma regression, Liu estimator, Liu parameter, maximum likelihood, multicollinearity
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:hj:diva-46441DOI: 10.1080/00949655.2018.1498502ISI: 000447551900001Scopus ID: 2-s2.0-85050377487OAI: oai:DiVA.org:hj-46441DiVA, id: diva2:1357276
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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