A new generalization of Weibull-exponential distribution with application
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Authors
Ramadan A. ZeinEldin
- Deanship of Scientific Research, Deanship of Scientific Research, Kingdom of Saudi Arabia.
- Institute of Statistical Studies and Research, Cairo University, Egypt.
M. Elgarhy
- Vice Presidency for Graduate Studies and Scientific Research, University of Jeddah, Jeddah,, Kingdom of Saudi Arabia.
Abstract
In this article, we will introduce a new five-parameter continuous model, called the Kumaraswamy Weibull exponential distribution based on Kumaraswamy Weibull-G family [A. S. Hassan, M. Elgarhy, Adv. Appl. Stat., \({\bf 48}\) (2016), 205--239]. The new model contains some new distributions as well as some former distributions. Various mathematical properties of this distribution are studied. General explicit expressions for the quantile function, expansion of distribution and density functions, moments, generating function, incomplete moments, conditional moments, residual life function, reversed residual life function, mean deviation, inequality measures, Rényi and q-entropies, probability weighted moments, and order statistics are obtained. The estimation of the model parameters is discussed using maximum likelihood method. The practical importance of the new distribution is demonstrated through real data sets where we compare it with several lifetime distributions.
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ISRP Style
Ramadan A. ZeinEldin, M. Elgarhy, A new generalization of Weibull-exponential distribution with application, Journal of Nonlinear Sciences and Applications, 11 (2018), no. 9, 1099--1112
AMA Style
ZeinEldin Ramadan A., Elgarhy M., A new generalization of Weibull-exponential distribution with application. J. Nonlinear Sci. Appl. (2018); 11(9):1099--1112
Chicago/Turabian Style
ZeinEldin, Ramadan A., Elgarhy, M.. "A new generalization of Weibull-exponential distribution with application." Journal of Nonlinear Sciences and Applications, 11, no. 9 (2018): 1099--1112
Keywords
- Exponential distribution
- Kumaraswamy Weibull-G family of distributions
- moments
- order statistics
- maximum likelihood estimation
MSC
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