The Generalized Odd Maxwell-Kumaraswamy Distribution: Its Properties and Applications
Issued Date
2024-01-01
Resource Type
ISSN
27051064
eISSN
27051056
Scopus ID
2-s2.0-85185973899
Journal Title
Contemporary Mathematics (Singapore)
Volume
5
Issue
1
Start Page
711
End Page
742
Rights Holder(s)
SCOPUS
Bibliographic Citation
Contemporary Mathematics (Singapore) Vol.5 No.1 (2024) , 711-742
Suggested Citation
Ishaq A.I., Panitanarak U., Abiodun A.A., Suleiman A.A., Daud H. The Generalized Odd Maxwell-Kumaraswamy Distribution: Its Properties and Applications. Contemporary Mathematics (Singapore) Vol.5 No.1 (2024) , 711-742. 742. doi:10.37256/cm.5120242888 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97455
Title
The Generalized Odd Maxwell-Kumaraswamy Distribution: Its Properties and Applications
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Author's Affiliation
Corresponding Author(s)
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Abstract
This study introduces a novel family of continuous probability distributions using Alzaatreh's technique to explore the Generalized Odd Maxwell-Generated (GOM-G) distribution family. Within the Generalized Odd Maxwell family, the GOM-Kumaraswamy distribution is presented as extended form of the Kumaraswamy distribution. The investigation thoroughly examines the cumulative distribution function, probability density function, hazard, and survival functions, as well as mixture representations of the Generalized Odd Maxwell-Kumaraswamy distribution. The suggested distribution's structural properties, including moments, skewness, kurtosis, probability-weighted moments, entropies, stress-strength models, and order statistics, are derived. Model parameters are estimated through the maximum likelihood method, and simulation studies evaluate the performance of maximum likelihood estimations using the quantile function. Employing two real-life datasets, goodness-of-fit measures such as Akaike Information Criterion (AIC), Corrected Akaike Information Criterion, Bayesian Information Criterion (BIC), Hannan-Quinn Information Criterion (HQIC), and chi-square goodness-of-fit tests demonstrate the adaptability and flexibility of the GOM-Kumaraswamy distribution against competing distributions, including Kumaraswamy-Kumaraswamy and Kumaraswamy-Burr III. The results reveal that the GOM-Kumaraswamy distribution exhibits the lowest AIC, CAIC, BIC, and HQIC values and the highest goodness-of-fit values compared to other models, suggesting its superiority as the preferred fit for both dataset forms. This proposed distribution contributes to practical applications, unveiling its potential significance in modeling real-world phenomena within the domains of hydrology and engineering.