Introducing the new arcsine-generator distribution family: An in-depth exploration with an illustrative example of the inverse weibull distribution for analyzing healthcare industry data
Tabassum Naz Sindhu, Anum Shafiq, Muhammad Bilal Riaz, Tahani A. Abushal, Hijaz Ahmad, Ehab M. Almetwally, Sameh Askar
Abstract
The study is about a novel Arcsin-function based generator of new families of distributions. We chose the inverse Weibull distribution as the reference distribution to see if the generator could be employed. This generator helps for developing a distribution called the novel Arcsin inverse Weibull. The main features of the suggested distribution have been taken into account. Some of the indicators used in this class include the density function, complete and incomplete moments, average deviation, and aging indicators. The model's parameters are determined using the maximum likelihood method in both simulations and data analysis. The effectiveness of the suggested model in the healthcare sector is demonstrated by analyzing five sets of data, revealing its superior fit compared to the traditional inverse sine model, which is associated with the inverse Weibull model.