Properties, estimation, and applications of the extended log-logistic distribution.

Autor: Kariuki V; Department of Mathematics, Pan African Institute of Basic Sciences, Technology and Innovation, 00200, Nairobi, Kenya., Wanjoya A; Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, 00200, Nairobi, Kenya., Ngesa O; Department of Mathematics, Statistics and Physical Science, Taita Taveta University, 80300, Voi, Kenya., Alharthi AS; Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, 21944, Taif, Saudi Arabia., Aljohani HM; Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, 21944, Taif, Saudi Arabia., Afify AZ; Department of Statistics, Mathematics, and Insurance, Benha University, Benha, 13511, Egypt. ahmed.afify@fcom.bu.edu.eg.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Sep 09; Vol. 14 (1), pp. 20967. Date of Electronic Publication: 2024 Sep 09.
DOI: 10.1038/s41598-024-68843-4
Abstrakt: This paper presents the exponentiated alpha-power log-logistic (EAPLL) distribution, which extends the log-logistic distribution. The EAPLL distribution emphasizes its suitability for survival data modeling by providing analytical simplicity and accommodating both monotone and non-monotone failure rates. We derive some of its mathematical properties and test eight estimation methods using an extensive simulation study. To determine the best estimation approach, we rank mean estimates, mean square errors, and average absolute biases on a partial and overall ranking. Furthermore, we use the EAPLL distribution to examine three real-life survival data sets, demonstrating its superior performance over competing log-logistic distributions. This study adds vital insights to survival analysis methodology and provides a solid framework for modeling various survival data scenarios.
(© 2024. The Author(s).)
Databáze: MEDLINE
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