A new two-parameter half-logistic distribution with numerical analysis and applications

Authors

  • Ahmad Abubakar Suleiman Department of Statistics, Aliko Dangote University of Science and Technology, Wudil, Nigeria https://orcid.org/0000-0001-8669-0572
  • Hanita Daud Fundamental and Applied Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia
  • Abdullahi G. Usman Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, 99138, Nicosia, Turkish Republic of Northern Cyprus
  • Sani I. Abba Department of Civil Engineering, Prince Mohammad Bin Fahd University, 31952, Al Khobar, Saudi Arabia
  • Mahmod Othman Department of Information System, Universitas Islam Indragiri, Tembilahan 29212, Indonesia
  • Mohammed Elgarhy Department of Basic Sciences, Higher Institute of Administrative Sciences, Belbeis, AlSharkia, Egypt

DOI:

https://doi.org/10.64497/jssci.2

Keywords:

Half-Logistic distribution, Odd Beta Prime family, distribution generalization, maximum likelihood estimation, lifetime data modeling, simulation study, order statistics, reliability

Abstract

The Half-Logistic (HL) distribution is a renowned lifetime distribution widely used in various domains, including reliability engineering, biological sciences, and actuarial research. Despite its fundamental simplicity and easy interpretation, the HL distribution has limitations in terms of flexibility for modeling datasets with heavy tails or varying skewness. To address these constraints, this paper introduces a new extension of the HL distribution based on the Odd Beta Prime (OBP) family. The newly developed distribution has two shape parameters and is named the Odd Beta Prime Half-Logistic (OBPHL) distribution. Its several distributional properties, including the probability density function, cumulative distribution function, survival function, hazard function, and quantile function, alongside other various distributional characteristics, such as moments, measures of skewness and kurtosis, entropy measure ( Rényi and Tsallis), and order statistics, are explored. The maximum likelihood estimation technique is used to estimate the parameters of the new model. The performance of the estimators under different sample sizes has been evaluated using a Monte Carlo simulation study. In addition, the proposed OBPHL distribution is applied to two real-world datasets. The results are compared against several classical distributions using standard model selection criteria. The OBPHL distribution consistently outperforms the competing models based on evaluation metrics. This research contributes a new flexible model to the field of statistical distribution theory and demonstrates its efficacy in practical applications.

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References

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Published

2025-06-29 — Updated on 2025-05-29

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How to Cite

Suleiman, A. A., Daud, H., Usman , A. G., Abba , S. I., Othman, M., & Elgarhy, M. (2025). A new two-parameter half-logistic distribution with numerical analysis and applications . Journal of Statistical Sciences and Computational Intelligence, 1(1), 1–28. https://doi.org/10.64497/jssci.2 (Original work published June 29, 2025)

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Vol. 1 No. 1 (2025): Volume 1, Issue 1 (2025)

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Copyright (c) 2025 Ahmad Abubakar Suleiman; Hanita Daud, Abdullahi G. Usman , Sani I. Abba , Mahmod Othman, Mohammed Elgarhy

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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