@article{
 title = {The novel Kumaraswamy power Frechet distribution with data analysis related to diverse scientific areas},
 type = {article},
 year = {2023},
 keywords = {Kumaraswamy-G family,Mean square error,Moments,Monte Carlo simulation,Power Frechet distribution},
 pages = {651-664},
 volume = {70},
 websites = {https://www.sciencedirect.com/science/article/pii/S1110016823001576},
 id = {3e0545d9-7e92-3775-9db8-6da660298b05},
 created = {2023-03-16T22:06:27.201Z},
 file_attached = {false},
 profile_id = {3d6b17c2-7de8-3a82-bf4f-ddb0e3081e5f},
 last_modified = {2023-03-16T22:06:27.201Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {The study’s major goal is to design a superior creative distribution by using the Kumaraswamy-G family of distributions to power Frechet distribution. The Kumaraswamy power Frechet distribution (KPFD) with four parameters is the full name of the revolutionary model. The distribution’s probability density function may take numerous forms and graphs and can be used to describe complicated data sets efficiently. Several features of the new distribution are obtained, including dependability, hazard rate, quantile, and moments. The estimation of the unknown parameters of KPFD are provided using the KPFD maximum likelihood estimation technique. Furthermore, a study was performed using the Monte Carlo simulation approach to test estimator accuracy regarding average bias (AB) and mean square error (MSE). Last but not least, two genuine data sets are supplied to compare the proposed model to existing models.},
 bibtype = {article},
 author = {Alsadat, Najwan and Ahmad, Aijaz and Jallal, Muzamil and Gemeay, Ahmed M and Meraou, Mohammed A and Hussam, Eslam and M.Elmetwally, Ehab and Hossain, Md. Moyazzem},
 doi = {https://doi.org/10.1016/j.aej.2023.03.003},
 journal = {Alexandria Engineering Journal}
}

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