Lomax tangent generalized family of distributions: Characteristics, simulations, and applications on hydrological-strength data
Sajid Mehboob Zaidi,
Zafar Mahmood,
Mintodê Nicodème Atchadé,
Yusra A. Tashkandy,
M.E. Bakr,
Ehab M. Almetwally,
Eslam Hussam,
Ahmed M. Gemeay,
Anoop Kumar
Affiliations
Sajid Mehboob Zaidi
Department of Statistics, Govt. Graduate College B.R., Bahawalpur, Pakistan
Zafar Mahmood
Government SE Graduate college Bahawalpur, Bahawalpur, Pakistan
Mintodê Nicodème Atchadé
National Higher School of Mathematics Genius and Modelization, National University of Sciences, Technologies, Engineering and Mathematics, Abomey, Republic of Benin
Yusra A. Tashkandy
Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
M.E. Bakr
Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Ehab M. Almetwally
Faculty of Business Administration, Delta University for Science and Technology, Gamasa 11152, Egypt
Eslam Hussam
Helwan University, Department of Mathematics, Faculty of Science, Cairo, Egypt; Corresponding author.
Ahmed M. Gemeay
Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt
Anoop Kumar
Department of Statistics, Faculty of Basic Science, Central University of Haryana, Mahendergarh 123031, India
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.
