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

作　　者：Sunisa Junnumtuam Sa-Aat Niwitpong Suparat Niwitpong 

机构地区：[1]Department ofApplied Statistics,Faculty of Applied Science,King Mongkut’sUniversity of TechnologyNorth Bangkok,Bangkok,10800,Thailand

出　　处：《Computer Modeling in Engineering & Sciences》2023年第5期1229-1254,共26页工程与科学中的计算机建模（英文）

基　　金：support from the National Science,Research and Innovation Fund (NSRF);King Mongkut’s University of Technology North Bangkok (Grant No.KMUTNB-FF-65-22).

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

关 键 词：Bayesian analysis confidence interval gibbs sampling random-walk metropolis zero-inflated count data 

分 类 号：R563.1[医药卫生—呼吸系统]