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Statistical Properties of Kumaraswamy-Generalized Exponentiated Exponential Distribution

by B. E. Mohammed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 4
Year of Publication: 2014
Authors: B. E. Mohammed
10.5120/16328-5602

B. E. Mohammed . Statistical Properties of Kumaraswamy-Generalized Exponentiated Exponential Distribution. International Journal of Computer Applications. 94, 4 ( May 2014), 1-8. DOI=10.5120/16328-5602

@article{ 10.5120/16328-5602,
author = { B. E. Mohammed },
title = { Statistical Properties of Kumaraswamy-Generalized Exponentiated Exponential Distribution },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 4 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number4/16328-5602/ },
doi = { 10.5120/16328-5602 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:16:40.467835+05:30
%A B. E. Mohammed
%T Statistical Properties of Kumaraswamy-Generalized Exponentiated Exponential Distribution
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 4
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we present a new class of distributions called kumaraswamy Generalized Exponentiated Exponential Distribution, that is based upon the cumulative distribution function of Kumaraswamy (1980) distribution, which is more flexible and is a natural generalization of the exponential, Exponentiated Exponential and kumaraswamy Generalized exponential distributions as special cases found in literature. Also, the analytical shapes of the corresponding probability density function and hazard rate function are derived with graphical illustrations. Expressions for the r^thmoments are calculated and the variation of the skewness and kurtosis measures is investigated. Likelihood estimators of the parameters are derived. Moreover, analysis of real data set, representing the breaking stress of carbon fibers, is conducted to demonstrate the usefulness of the proposed distribution.

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Index Terms

Computer Science
Information Sciences

Keywords

Kumaraswamy Distribution Maximum likelihood estimation Akaike information criterion Baysian information criterion Consistent Akaike Information Criteria Kaplan-Meier estimator likelihood ratio test p-p plot.

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