Statistical inference of unified hybrid censoring scheme for generalized inverted exponential distribution with application to COVID-19 data
Mustafa M. Hasaballah, Yusra Tashkandy, Oluwafemi Samson Balogun, M. E. Bakr
Abstract
Using a unified hybrid censoring scheme, this study explores statistical inferences for products with lifetimes following the generalized inverted exponential distribution. The model’s parameters are estimated using the maximum likelihood approach. In addition, likelihood functions and asymptotic theories are employed to generate approximate confidence intervals. Moreover, Bayesian estimates based on classical likelihood functions are investigated, considering both asymmetric and symmetric loss functions with prior information. It is recommended to approximate the Bayes estimates using Gibbs sampling, which utilizes the Markov chain Monte Carlo technique to establish credible intervals for the parameters. A numerical example is provided to demonstrate the effectiveness of the proposed methods. Furthermore, a simulation study is conducted to illustrate the confidence intervals and the statistical characteristics of the parameters.