Bayesian Computation for the Parameters of a Zero-Inflated Cosine Geometric Distribution with Application to COVID-19 Pandemic Data

A new three-parameter discrete distribution called the zero-inflated cosine geometric (ZICG) is proposed for first time herein. It can be used to analyze over-dispersed count data with excess zeros. The basic statistical properties of distribution, such as moment generating function, mean, and variance are presented. Furthermore, confidence intervals constructed by using Wald, Bayesian, highest posterior density (HPD) methods estimate true parameters ZICG distribution. Their efficacies were investigated both simulation real-world comprising number daily COVID-19 positive cases at Olympic Games in Tokyo 2020. results show that HPD interval performed better than other terms coverage probability average length most studied.