Abstract

In this paper, we introduced a new extension model of distribution. This model is the Power weighted Gompertz distribution (PWG), it is generated by the power transformation method. We obtained some statistical properties of the new model, including moments, moment generating function, some types of entropies, rsidual life and reversed residual life functions, and Bonferroni and Lorenz curves. Estimation of the parameters of extended distribution is obtained by the method of maximum likelihood. To check the usefulness of new model, we applied two real data set and used some goodness of fit statistics. We illustrated the versatility of proposed model to fit and model data and confirmed that this model provide a better fit than some other very well-known distributions.

Keywords: Power transformation, Gompertz distribution, moments, entropies, Maximum likelihood estimation

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How to Cite
[1]
Rawabi Salem Wasel Al-ahmadi 2021. The Power Weighted Gompertz Model. International Journal of Science and Engineering Invention. 7, 01 (Jan. 2021), 1–14. DOI:https://doi.org/10.23958/ijsei/vol07-i01/212.

Copyrights & License

Copyright © 2021 Rawabi Salem Wasel Al-ahmadi this is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.