A New Beta Power Generator for Continuous Random Variable: Features and Inference to Model Asymmetric Data

Authors

Mohamed S. Eliwa, Department of Statistics and Operation Research, College of Science, Qassim University, Buraydah 51482, Saudi Arabia\\ Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt\\ Department of Mathematics, International Telematic University Uninettuno, I-00186 Rome, ItalyFollow
Zubair Ahmad, Department of Statistics, Quaid-i-Azam University, Islamabad 44000, PakistanFollow
Manal H. Alabdulhadi, Department of Mathematics, College of Science and Arts, University, Bukairiayh 51452, Qassim, Saudi ArabiaFollow
Faridoon Khan, PIDE School of Economics, PIDE Islamabad 44000, PakistanFollow

Abstract

Statistical methodologies have broad applications in sports and other exercise sciences. These methods can be used to predict the winning probability of a team or individual in a match. Due to the applicability of the statistical methods in sports, this paper introduces a new method of obtaining statistical distributions. The new method is called a novel beta power-L family of distributions. Some mathematical characteristics of the new family are obtained. Based on the novel beta power-L family, a special model, namely, a novel beta power Weibull model is studied. Finally, the applicability/usefulness of the novel beta power Weibull distribution is shown by analyzing the time-to-even data taken from different football matches during 1964-2018. The data consist of seventy-eight observations and is representing the waiting time duration of the fastest goal scored ever in the history of football. The fitting results of the novel beta power Weibull distribution are compared with other models. Based on three model selection criteria, it is observed that the proposed novel beta power Weibull model provides a close fit to the waiting time data.

Recommended Citation

S. Eliwa, Mohamed; Ahmad, Zubair; H. Alabdulhadi, Manal; and Khan, Faridoon (2023) "A New Beta Power Generator for Continuous Random Variable: Features and Inference to Model Asymmetric Data," Information Sciences Letters: Vol. 12 : Iss. 4 , PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol12/iss4/16