Statistical modelling for a new family of generalized distributions with real data applications

Author:

Bakr M. E.1,Al-Babtain Abdulhakim A.1,Mahmood Zafar2,Aldallal R. A.3,Khosa Saima Khan4,Abd El-Raouf M. M.5,Hussam Eslam6,Gemeay Ahmed M.7

Affiliation:

1. Department of Statistics and Operation Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia

2. Government Associate College, Khairpur Tamewali, Bahawalpur, Pakistan

3. College of Business Administration in Hotat bani Tamim, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia

4. Department of Mathematics and Statistics University of Saskatchewan, Saskatoon, SK, Canada

5. Basic and Applied Science Institute, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandria, Egypt

6. Department of Mathematics, Faculty of Science, Helwan University, Cairo, Egypt

7. Department of Mathematics, Faculty of Science, Tanta University, Tanta 31527, Egypt

Abstract

<abstract><p>The modern trend in distribution theory is to propose hybrid generators and generalized families using existing algebraic generators along with some trigonometric functions to offer unique, more flexible, more efficient, and highly productive G-distributions to deal with new data sets emerging in different fields of applied research. This article aims to originate an odd sine generator of distributions and construct a new G-family called "The Odd Lomax Trigonometric Generalized Family of Distributions". The new densities, useful functions, and significant characteristics are thoroughly determined. Several specific models are also presented, along with graphical analysis and detailed description. A new distribution, "The Lomax cosecant Weibull" (LocscW), is studied in detail. The versatility, robustness, and competency of the LocscW model are confirmed by applications on hydrological and survival data sets. The skewness and kurtosis present in this model are explained using modern graphical methods, while the estimation and statistical inference are explored using many estimation approaches.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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