Statistical inference of the inverted exponentiated Lomax distribution using generalized order statistics with application to COVID-19

Abstract

In this study, the parameters of the inverted exponentiated Lomax distribution via generalized order statistics are assessed using Bayesian and maximum likelihood approaches. The maximum likelihood estimators along with approximate confidence intervals are calculated. Under the squared error loss function, the Bayesian estimator, percentile bootstrap, and bootstrap-t credible periods are produced. Furthermore, the proposed estimators are dedicated to schemes such as type-II censored ordinary order statistics joint density function. A numerical simulation is used to assess the behavior and sensitivity of the estimates for various sample sizes. From the posterior distributions, the Metropolis–Hastings technique is used to generate Markov chain Monte Carlo samples. We utilize this technique to examine a current dataset of interest: daily cases of COVID-19 instances detected in Saudi Arabia from May 31 to October 28, 2020 (inclusive). In the future, the proposed methodology could be useful for analyzing data on COVID-19 instances in other countries for comparative studies.