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Kumaraswamy transmuted exponentiated additive Weibull distribution
International Journal of Statistics and Probability • 2016
Publication Information
Authors
Zohdy M. Nofal;Ahmed Z. Afify;Haitham M. Yousof;Daniele C. T. Granzotto;Francisco Louzada
Keywords
Additive Weibull distribution;order statistics;maximum likelihood estimation;Renyi and q entropies;goodness of fit; moment;generating function
Journal
International Journal of Statistics and Probability
Publisher
Canadian Center of Science and Education
Volume
5
Issue
2
Pages
78-99
publication.type
International
Paper Link
Open Link
Supplementary Materials
Not Available
Abstract
This paper introduces a new lifetime model which is a generalization of the transmuted exponentiated additive Weibull
distribution by using the Kumaraswamy generalized (Kw-G) distribution. With the particular case no less than seventy
nine sub models as special cases, the so-called Kumaraswamy transmuted exponentiated additive Weibull distribution,
introduced by Cordeiro and de Castro (2011) is one of this particular cases. Further, expressions for several probabilistic
measures are provided, such as probability density function, hazard function, moments, quantile function, mean, variance
and median, moment generation function, R ´ enyi and q entropies, order estatistics, etc. Inference is maximum likelihood
based and the usefulness of the model is showed by using a real dataset.
distribution by using the Kumaraswamy generalized (Kw-G) distribution. With the particular case no less than seventy
nine sub models as special cases, the so-called Kumaraswamy transmuted exponentiated additive Weibull distribution,
introduced by Cordeiro and de Castro (2011) is one of this particular cases. Further, expressions for several probabilistic
measures are provided, such as probability density function, hazard function, moments, quantile function, mean, variance
and median, moment generation function, R ´ enyi and q entropies, order estatistics, etc. Inference is maximum likelihood
based and the usefulness of the model is showed by using a real dataset.