Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data
This article investigates a survival analysis under randomly censored mortality distribution. From the perspective of frequentist, we derive the point estimations through the method of maximum likelihood estimation. Furthermore, approximate confidence intervals for the parameters are constructed bas...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/8300753 |
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| author | Rashad M. EL-Sagheer Mohamed S. Eliwa Khaled M. Alqahtani Mahmoud EL-Morshedy |
| author_facet | Rashad M. EL-Sagheer Mohamed S. Eliwa Khaled M. Alqahtani Mahmoud EL-Morshedy |
| author_sort | Rashad M. EL-Sagheer |
| collection | DOAJ |
| description | This article investigates a survival analysis under randomly censored mortality distribution. From the perspective of frequentist, we derive the point estimations through the method of maximum likelihood estimation. Furthermore, approximate confidence intervals for the parameters are constructed based on the asymptotic distribution of the maximum likelihood estimators. Besides, two parametric bootstraps are implemented to construct the approximate confidence intervals for the unknown parameters. In Bayesian framework, the Bayes estimates of the unknown parameters are evaluated by applying the Markov chain Monte Carlo technique, and highest posterior density credible intervals are also carried out. In addition, the Bayes inference based on symmetric and asymmetric loss functions is obtained. Finally, Monte Carlo simulation is performed to observe the behavior of the proposed methods, and a real data set of COVID-19 mortality rate is analyzed for illustration. |
| format | Article |
| id | doaj-art-12b7b479cb134178b79f402c8b977242 |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-12b7b479cb134178b79f402c8b9772422025-02-03T05:53:28ZengWileyJournal of Mathematics2314-47852022-01-01202210.1155/2022/8300753Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 DataRashad M. EL-Sagheer0Mohamed S. Eliwa1Khaled M. Alqahtani2Mahmoud EL-Morshedy3Department of MathematicsDepartment of Statistics and Operation ResearchDepartment of MathematicsDepartment of MathematicsThis article investigates a survival analysis under randomly censored mortality distribution. From the perspective of frequentist, we derive the point estimations through the method of maximum likelihood estimation. Furthermore, approximate confidence intervals for the parameters are constructed based on the asymptotic distribution of the maximum likelihood estimators. Besides, two parametric bootstraps are implemented to construct the approximate confidence intervals for the unknown parameters. In Bayesian framework, the Bayes estimates of the unknown parameters are evaluated by applying the Markov chain Monte Carlo technique, and highest posterior density credible intervals are also carried out. In addition, the Bayes inference based on symmetric and asymmetric loss functions is obtained. Finally, Monte Carlo simulation is performed to observe the behavior of the proposed methods, and a real data set of COVID-19 mortality rate is analyzed for illustration.http://dx.doi.org/10.1155/2022/8300753 |
| spellingShingle | Rashad M. EL-Sagheer Mohamed S. Eliwa Khaled M. Alqahtani Mahmoud EL-Morshedy Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data Journal of Mathematics |
| title | Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data |
| title_full | Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data |
| title_fullStr | Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data |
| title_full_unstemmed | Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data |
| title_short | Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data |
| title_sort | asymmetric randomly censored mortality distribution bayesian framework and parametric bootstrap with application to covid 19 data |
| url | http://dx.doi.org/10.1155/2022/8300753 |
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