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Modified Alpha Power Transformed Inverse Power Lomax Distribution with applications |
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PP: 17-52 |
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doi:10.18576/jsapl/120102
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Author(s) |
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Adote ́ Herve ́ Gildas Akueson,
Mahoule ́ Jude Bogninou,
Arcadius Yves Justin Akossou,
Hamidou Bah,
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Abstract |
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| This research introduces the Modified Alpha Power Transformed Inverse Power Lomax Distribution (MAPT-IPLD), a novel and flexible distribution designed to model complex datasets effectively. By integrating the modified alpha power transformation with the inverse power Lomax distribution, the MAPT-IPLD emerges as a robust extension capable of addressing diverse statistical modeling challenges.We present a thorough exploration of the theoretical properties of the MAPT-IPLD, including its stochastic functions, quantile function, moments, probability-weighted moments, and Re ́nyi entropy. Additionally, we derive closed-form expressions for key statistical measures, providing a strong foundation for practical applications.To validate the practical utility of the MAPT-IPLD, we apply it to multiple real-world datasets. The proposed distribution demonstrates superior performance compared to existing models, as evidenced by its ability to achieve the lowest values across a range of information criteria, such as AIC, BIC, HQIC, and K-S statistics. Furthermore, the MAPT-IPLD delivers excellent goodness-of-fit, as visualized through estimated density plots and Q-Q plots, showcasing its ability to accurately capture underlying data structures.These results highlight the MAPT-IPLD’s potential as a versatile and reliable tool for statistical modeling across various domains, including reliability analysis, survival studies, and environmental data modeling. By offering a balance between flexibility and parsimony, the MAPT-IPLD sets a new benchmark in the field of statistical distributions.
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