Benha University

محمد سويلم السيد حامد

مدرس

كلية التجارة الاحصاءات والرياضيات والتأمينات

A New Transmuted Additive Weibull Distribution: Based On A New Method For Adding A Parameter To A Family Of Distributions

International Journal of Applied Mathematical Sciences • 2020
العودة
معلومات البحث
المؤلفون Mohamed S. Hamed, et
الكلمات المفتاحية transmutation; survival function; exponentiated exponential; order statistics; maximum likelihood estimation.
المجلة العلمية International Journal of Applied Mathematical Sciences
الناشر Research India Publications
المجلد 8
العدد 1
الصفحات 31-54
publication.type International
رابط البحث Open Link
المواد المرفقة Not Available
الملخص
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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