Comparing Parameter Estimates Obtained by Simulation Study and Real Life Data from the Two-Parameter Gamma Model
International Journal of Statistical Distributions and Applications
Volume 3, Issue 2, June 2017, Pages: 13-17
Received: Aug. 27, 2016; Accepted: Nov. 30, 2016; Published: May 6, 2017
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Authors
A. M. Yahaya, Department of Mathematics and Statistics, University of Maiduguri, Maiduguri, Nigeria
N. P. Dibal, Department of Mathematics and Statistics, University of Maiduguri, Maiduguri, Nigeria
H. R. Bakari, Department of Mathematics and Statistics, University of Maiduguri, Maiduguri, Nigeria
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Abstract
The aim of this study was to employ Maximum Likelihood (MLE) jointly with a numerical Method (Newton Raphson method) to obtain parameter estimates from the two-parameter Gamma model. The profile likelihood of the two-parameter Gamma model was also put into consideration. The methods were demonstrated using simulation studies and real life data considering data sets generated by R statistical software for different sample sizes. Standard errors were computed and 5 % Wald-confidence interval was constructed for the estimates of the model. The result of the study shows that Maximum Likelihood Estimation (MLE) jointly with Newton Raphson method was more efficient for estimating parameters of the Gamma model in simulation study than real life data. The study recommends that parameter estimates from the two-parameter Gamma model should be obtained by employing Maximum Likelihood Estimation jointly with Newton Raphson Method.
Keywords
Parameter Estimation, Two-Parameter Gamma Model, Profile Likelihood, Maximum Likelihood Estimation, Newton Raphson Method
To cite this article
A. M. Yahaya, N. P. Dibal, H. R. Bakari, Comparing Parameter Estimates Obtained by Simulation Study and Real Life Data from the Two-Parameter Gamma Model, International Journal of Statistical Distributions and Applications. Vol. 3, No. 2, 2017, pp. 13-17. doi: 10.11648/j.ijsd.20170302.11
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Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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