ESTIMATING THE PARAMETERS OF GENERALIZED LOGISTIC DISTRIBUTION VIA GENETIC ALGORITHM BASED ON REDUCED SEARCH SPACE

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dc.contributor.authorYalçınkaya, Abdullah
dc.contributor.authorKılıç, Adil
dc.contributor.authorŞeno?lu, Birdal
dc.date.accessioned2025-01-21T16:27:34Z
dc.date.available2025-01-21T16:27:34Z
dc.date.issued2024
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIn this study, maximum likelihood (ML) estimates of the parameters of generalized logistic (GL) distribution are obtained using the genetic algorithm (GA) based on the reduced search space (RSS) proposed by (Yalçınkaya et al. Swarm and Evolutionary Computation 38, 127–138, 2018). RSS is defined in terms of the confidence intervals based on modified maximum likelihood (MML) estimators. MML estimators are the explicit functions of the sample observations and asymptotically equivalent to the ML estimators, (Tiku, Biometrika 54, 155–165, 1967). To see the effectiveness of RSS, we examine the performance of GA based on RSS against GA based on fixed search space (FSS). The efficiencies of these estimators are also compared with the classical ML estimators using Newton–Raphson (NR) algorithm via Monte Carlo simulation study. MML estimators of GL distribution parameters are also included into the study to show the performance of the non-iterative MML methodology against the iterative ML methodology. At the end of the study, a real-life example is given to see the implementation of the proposed methodology. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
dc.identifier.doi10.1007/s10958-024-07088-y
dc.identifier.issn1072-3374
dc.identifier.scopus2-s2.0-85192532352
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://doi.org/10.1007/s10958-024-07088-y
dc.identifier.urihttps://hdl.handle.net/20.500.12587/23359
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Mathematical Sciences (United States)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241229
dc.subjectEfficiency; Generalized logistic; Genetic algorithm; Maximum likelihood; Modified maximum likelihood; Newton–Raphson
dc.titleESTIMATING THE PARAMETERS OF GENERALIZED LOGISTIC DISTRIBUTION VIA GENETIC ALGORITHM BASED ON REDUCED SEARCH SPACE
dc.typeArticle

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