On six-parameter Frechet distribution: properties and applications
Pakistan Journal of Statistics and Operation Research • 2016
معلومات البحث
المؤلفون
Haitham M. Yousof;Ahmed Z. Afify;Abd El Hadi N. Ebraheim;G. G. Hamedani;Nadeem Shafique Butt
الكلمات المفتاحية
Moments of residual life;Goodness-of-fit;Order Statistics; Maximum
Likelihood Estimation
المجلة العلمية
Pakistan Journal of Statistics and Operation Research
الناشر
Punjab University Press
المجلد
12
العدد
2
الصفحات
281-299
publication.type
International
رابط البحث
Open Link
المواد المرفقة
Not Available
الملخص
This paper introduces a new generalization of the transmuted Marshall-Olkin Fréchet distribution of Afify
et al. (2015), using Kumaraswamy generalized family. The new model is referred to as Kumaraswamy
transmuted Marshall-Olkin Fréchet distribution. This model contains sixty two sub-models as special cases
such as the Kumaraswamy transmuted Fréchet, Kumaraswamy transmuted Marshall -Olkin, generalized
inverse Weibull and Kumaraswamy Gumbel type II distributions, among others. Various mathematical
properties of the proposed distribution including closed forms for ordinary and incomplete moments,
quantile and generating functions and Rényi and -entropies are derived. The unknown parameters of the
new distribution are estimated using the maximum likelihood estimation. We illustrate the importance of
the new model by means of two applications to real data sets.
et al. (2015), using Kumaraswamy generalized family. The new model is referred to as Kumaraswamy
transmuted Marshall-Olkin Fréchet distribution. This model contains sixty two sub-models as special cases
such as the Kumaraswamy transmuted Fréchet, Kumaraswamy transmuted Marshall -Olkin, generalized
inverse Weibull and Kumaraswamy Gumbel type II distributions, among others. Various mathematical
properties of the proposed distribution including closed forms for ordinary and incomplete moments,
quantile and generating functions and Rényi and -entropies are derived. The unknown parameters of the
new distribution are estimated using the maximum likelihood estimation. We illustrate the importance of
the new model by means of two applications to real data sets.
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