Secant Topp-Leone family of distributions : Properties, regression and applications

Nanga, Salifu; Nasiru, Suleman; Dioggban, Jakperik

doi:10.47974/JSMS-1237

Open Access Research Article

Secant Topp-Leone family of distributions : Properties, regression and applications

^*Salifu NangaCorresponding authorsnanga@uhas.edu.gh nanga.salifu@gmail.comDepartment of Basic SciencesSchool of Basic and Biomedical ScienceUniversity of Health and Allied SciencesH0E, GhanaView full profile → , Suleman Nasirusulemanstat@gmail.com snasiru@cktutas.edu.ghDepartment of Statistics and Actuarial ScienceSchool of Mathematical SciencesC. K. Tedam University of Technology and Applied SciencesNavrongo BOL, Ghana0000-0001-6652-4251View full profile → , Jakperik Dioggbanjdioggban@gmail.comDepartment of BiometrySchool of Mathematical SciencesC. K. Tedam University of Technology and Applied SciencesNavrongo BOL, GhanaView full profile →

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pp. 1473–1497Vol. 27Issue 7October 2024DOI: 10.47974/JSMS-1237XML

Received:: 10 May 2023
Published Online:: 30 Nov 2024
Article type:: Research Article
Language:: EN
Article no.:: JSMS-1237
Pages:: 1473–1497

Abstract

The Secant Topp-Leone (STL) generator, which is developed by combining the TL and secant distribution families, is discussed in this paper. We address various statistical characteristics of this new family. We also present five special distributions within this family and demonstrate their flexibility. These distributions’ parameters are estimated using the maximum likelihood estimation (MLE) technique, and Monte Carlo simulations are used to assess the estimation technique’s accuracy. The choice of estimation method used in the study was validated by results from the Monte Carlo experiment’s biases, RMSEs, average width, and coverage probability. Using two real datasets, the Secant Topp-Leone Burr III (STLBIII) distribution performs better than other well-known distributions. Additionally, the natural logarithm of secant Topp-Leone Burr XII (LSTLBXII) location-scale regression line equation is developed. On a given dataset, the developed model outperformed three other existing models.

Keywords

Location-scale Moments MLE Secant-generated family Topp-Leone-generated family Real datasets

Subject Classifications

(2010) 60E0562F1062E15

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