A novel extension of half-logistic distribution with statistical inference, estimation and applications

A A Bhat; S P Ahmad; Ahmed M Gemeay; Abdisalam Hassan Muse; M E Bakr; Oluwafemi Samson Balogun

doi:10.1038/s41598-024-53768-9

A novel extension of half-logistic distribution with statistical inference, estimation and applications

Sci Rep. 2024 Feb 21;14(1):4326. doi: 10.1038/s41598-024-53768-9.

Authors

A A Bhat¹, S P Ahmad², Ahmed M Gemeay³, Abdisalam Hassan Muse⁴, M E Bakr⁵, Oluwafemi Samson Balogun⁶

Affiliations

¹ Department of Mathematical Sciences, Islamic University of Science and Technology, Awantipora, 192122, India.
² Department of Statistics, University of Kashmir, Srinagar, 19006, India.
³ Department of Mathematics, Faculty of Science, Tanta University, Tanta, 31527, Egypt.
⁴ Faculty of Science and Humanities, School of Postgraduate Studies and Research (SPGSR), Amoud University, Borama, 25263, Somalia. abdisalam.hassan@amoud.edu.so.
⁵ Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, 11451, Riyadh, Saudi Arabia.
⁶ Department of Computing, University of Eastern Finland, 70211, Joensuu, Finland.

Abstract

In the present study, we develop and investigate the odd Frechet Half-Logistic (OFHL) distribution that was developed by incorporating the half-logistic and odd Frechet-G family. The OFHL model has very adaptable probability functions: decreasing, increasing, bathtub and inverted U shapes are shown for the hazard rate functions, illustrating the model's capacity for flexibility. A comprehensive account of the mathematical and statistical properties of the proposed model is presented. In estimation viewpoint, six distinct estimation methodologies are used to estimate the unknown parameters of the OFHL model. Furthermore, an extensive Monte Carlo simulation analysis is used to evaluate the effectiveness of these estimators. Finally, two applications to real data are used to demonstrate the versatility of the suggested method, and the comparison is made with the half-logistic and some of its well-known extensions. The actual implementation shows that the suggested model performs better than competing models.

Keywords: Estimation methods; Half logistic distribution; Monte Carlo simulation; Odd Frechet-G family; Quantile function; Statistical properties.