The New Topp-Leone Exponentiated Half Logistic-Gompertz-G Family of Distributions with Applications

  • Wellington Charumbira Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Botswana; Department of Applied Mathematics and Statistics, Midlands State University, Gweru, Zimbabwe
  • Broderick Oluyede Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Botswana
  • Fastel Chipepa Department of Mathematics and Statistical Sciences, Botswana International University of Science and Technology, Botswana
DOI: https://doi.org/10.19139/soic-2310-5070-2238
Keywords: Maximum likelihood, Moments, Exponentiated-Half-Logistic Distribution, Stochastic Ordering, Gompertz Distribution, Entropy, Topp-Leone Distribution.

Abstract

This research introduces a new family of distributions (FoD) titled the Topp-Leone Exponentiated-Half-Logistic-Gompertz-G (TL-EHL-Gom-G) distribution. The study explores a variety of statistical properties of the developed family, such as the quantile function, series expansion, order statistics, entropy, stochastic orders and moments. Through Monte Carlo simulations, various estimation techniques were compared, including the least squares (LS), Anderson Darling (AD), maximum likelihood (ML) and Cram\'er-von-Mises (CVM) methods via root mean square error (RMSE) and average bias (Abias). The results indicated that the ML estimation method performed better than other methods, hence, the selection for estimating the model parameters. To showcase the usefulness, robustness and applicability of the model, we applied it to three real-life data, including dataset with censored observations. The TL-EHL-Gom-W distribution, a special case of the TL-EHL-Gom-G FoD showed superiority over nested and non-nested models.
Published
2025-06-10
How to Cite
Charumbira, W., Oluyede, B., & Chipepa, F. (2025). The New Topp-Leone Exponentiated Half Logistic-Gompertz-G Family of Distributions with Applications. Statistics, Optimization & Information Computing, 14(2), 486-514. https://doi.org/10.19139/soic-2310-5070-2238
Issue
Vol 14 No 2 (2025)
Section
Research Articles
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