DOI : https://doi.org/10.18517/ijaseit.12.6.16985

The Kumaraswamy Distribution: Statistical Properties and Application

Duraid Hussein Badra (1), Alyaa Hashem Mohammed (2)
(1) Statistics Department, Collage of administration and Economics, Basrah University, Iraq
(2) Statistics Department, Collage of administration and Economics, Mustansiriyah University, Iraq
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How to cite (IJASEIT) :
Badra, Duraid Hussein, and Alyaa Hashem Mohammed. “The Kumaraswamy Distribution: Statistical Properties and Application”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 6, Dec. 2022, pp. 2415-20, doi:10.18517/ijaseit.12.6.16985.
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Modeling and analyzing lifetime data is an important aspect of statistical work in various scientific and technological fields such as medicine, engineering, insurance, and finance. The modeling and analysis of lifetimes is an important aspect of statistical work in various scientific and technological fields. In recent years, inverted Kumaraswamy distribution has been used quite effectively to model many lifetime data. The most broadly applied statistical distribution is Kumaraswamy distribution in hydrological problems and many natural phenomena. The Kumaraswamy distribution (KD) is widely applied for modeling data in practical domains, such as medicine, engineering, economics, and physics. The present work proposes the Bayesian estimators of KD parameters through the use of type-II censoring data in this research the problem to estimate the unknown parameters of Kumaraswamy distribution with two parameters θ and λ, these estimates are a maximum likelihood of ordered observation and the Bayesian for the parameter of the Kumaraswamy distribution (KUD) depended on ranked set sampling (RSS) techniques. Both the simulated are inserted into real-life data sets and are considered to make a comparison between the estimation based on Maximum Likelihood estimators and Bayesian Estimation methods based on (RSS) techniques. For comparison purposes, we employed (100) mean square error and the criteria like AICC (Akaike information corrected criterion). Finally, the importance and flexibility of the new model of real data set are proved empirically.

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