Lomax tangent generalized family of distributions: Characteristics, simulations, and applications on hydrological-strength data.

Autor:	Zaidi SM; Department of Statistics, Govt. Graduate College B.R., Bahawalpur, Pakistan., Mahmood Z; Government SE Graduate college Bahawalpur, Bahawalpur, Pakistan., Atchadé MN; National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Republic of Benin., Tashkandy YA; Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia., Bakr ME; Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia., Almetwally EM; Faculty of Business Administration, Delta University for Science and Technology, Gamasa 11152, Egypt., Hussam E; Helwan University, Department of Mathematics, Faculty of Science, Cairo, Egypt., Gemeay AM; Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt., Kumar A; Department of Statistics, Faculty of Basic Science, Central University of Haryana, Mahendergarh 123031, India.
Jazyk:	angličtina
Zdroj:	Heliyon [Heliyon] 2024 May 31; Vol. 10 (12), pp. e32011. Date of Electronic Publication: 2024 May 31 (Print Publication: 2024).
DOI:	10.1016/j.heliyon.2024.e32011
Abstrakt:	This article proposes and discusses a novel approach for generating trigonometric G-families using hybrid generalizers of distributions. The proposed generalizer is constructed by utilizing the tangent trigonometric function and distribution function of base model G ( x ) . The newly proposed family of uni-variate continuous distributions is named the "Lomax Tangent Generalized Family of Distributions (LT-G)" and structural-mathematical-statistical properties are derived. Some special and sub-models of the proposed family are also presented. A Weibull-based model, 'The Lomax Tangent Weibull (LT-W) Distribution," is discussed and the plots of density (pdf) and hazard (hrf) functions are also explained. Model parameter estimates are estimated by employing the maximum likelihood estimation (MLE) procedure. The accuracy of the MLEs is evaluated through Monte Carlo simulation. Last but not least, to demonstrate the flexibility and potential of the proposed distribution, two actual hydrological and strength data sets are analyzed. The obtained results are compared with well-known, competitive, and related existing distributions. Competing Interests: The authors declare no conflict of interest. (© 2024 The Authors.)
Databáze:	MEDLINE
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