Statistical inference of the inverted exponentiated Lomax distribution using generalized order statistics with application to COVID-19
Source abstract
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.
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Research context
OpenAlex topics: Statistical Distribution Estimation and Applications · Statistical Methods and Bayesian Inference · Bayesian Methods and Mixture Models
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Statistics and Insurance, Faculty of Commerce, Arish University 1 , Al-Arish 45511, Egypt
Faculty of Graduate Studies for Statistical Research, Cairo University 2 , 5 Dr. Ahmed Zewail Street, Giza 12613, Egypt
Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU) 3 , Riyadh 11432, Saudi Arabia
Faculty of Business Administration, Delta University of Science and Technology 4 , Gamasa 11152, Egypt
Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University 5 , P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mathematical Sciences Department, College of Applied Sciences, Umm Al-Qura University 6 , Makkah, Saudi Arabia
Funding
Deanship of Scientific Research, Princess Nourah Bint Abdulrahman University
References (52)
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