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Dr. Mohamed Sewalim El-sayed Hamed :: Publications:

Title:
A New Transmuted Additive Weibull Distribution: Based On A New Method For Adding A Parameter To A Family Of Distributions
Authors: Mohamed S. Hamed, et
Year: 2020
Keywords: transmutation; survival function; exponentiated exponential; order statistics; maximum likelihood estimation.
Journal: International Journal of Applied Mathematical Sciences
Volume: 8
Issue: 1
Pages: 31-54
Publisher: Research India Publications
Local/International: International
Paper Link:
Full paper Mohamed Sewalim El-sayed Hamed_A New Transmuted Additive Weibull Distribution.pdf
Supplementary materials Not Available
Abstract:

This paper introduces a new generalization of the transmuted additive Weibull distribution by Elbatal and Aryal [10], based on a new family of lifetime distribution. We refer to the new distribution as a new transmuted additive Weibull (NTAW) distribution. The new model contains some of lifetime distributions as special cases such as the transmuted additive Weibull, exponentiated modified Weibull, exponentiated Weibull, exponentiated exponential, transmuted Weibull, Rayleigh, linear failure rate and exponential distributions, among others. The properties of the new model are discussed and the maximum likelihood estimation is used to evaluate the parameters. Explicit expressions are derived for the moments and examine the order statistics. An application to real data set is finally presented for illustration. This paper introduces a new generalization of the transmuted additive Weibull distribution by Elbatal and Aryal [10], based on a new family of lifetime distribution. We refer to the new distribution as a new transmuted additive Weibull (NTAW) distribution. The new model contains some of lifetime distributions as special cases such as the transmuted additive Weibull, exponentiated modified Weibull, exponentiated Weibull, exponentiated exponential, transmuted Weibull, Rayleigh, linear failure rate and exponential distributions, among others. The properties of the new model are discussed and the maximum likelihood estimation is used to evaluate the parameters. Explicit expressions are derived for the moments and examine the order statistics. An application to real data set is finally presented for illustration.

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