Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19
Dynamic cumulative residual entropy is a recent measure of uncertainty which plays a substantial role in reliability and survival studies. This article comes up with Bayesian estimation of the dynamic cumulative residual entropy of Pareto Ⅱ distribution in case of non-informative and informative pri...
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| Format: | Article |
| Language: | English |
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AIMS Press
2021-01-01
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| Series: | AIMS Mathematics |
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| Online Access: | http://www.aimspress.com/article/doi/10.3934/math.2021133?viewType=HTML |
| _version_ | 1818612204202622976 |
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| author | Abdullah Ali H. Ahmadini Amal S. Hassan Ahmed N. Zaky Shokrya S. Alshqaq |
| author_facet | Abdullah Ali H. Ahmadini Amal S. Hassan Ahmed N. Zaky Shokrya S. Alshqaq |
| author_sort | Abdullah Ali H. Ahmadini |
| collection | DOAJ |
| description | Dynamic cumulative residual entropy is a recent measure of uncertainty which plays a substantial role in reliability and survival studies. This article comes up with Bayesian estimation of the dynamic cumulative residual entropy of Pareto Ⅱ distribution in case of non-informative and informative priors. The Bayesian estimator and the corresponding credible interval are obtained under squared error, linear exponential (LINEX) and precautionary loss functions. The Metropolis-Hastings algorithm is employed to generate Markov chain Monte Carlo samples from the posterior distribution. A simulation study is done to implement and compare the accuracy of considered estimates in terms of their relative absolute bias, estimated risk and the width of credible intervals. Regarding the outputs of simulation study, Bayesian estimate of dynamic cumulative residual entropy under LINEX loss function is preferable than the other estimates in most of situations. Further, the estimated risks of dynamic cumulative residual entropy decrease as the value of estimated entropy decreases. Eventually, inferential procedure developed in this paper is illustrated via a real data. |
| first_indexed | 2024-12-16T15:42:30Z |
| format | Article |
| id | doaj.art-841e5f46840a4d42af4dc2ecfb856846 |
| institution | Directory Open Access Journal |
| issn | 2473-6988 |
| language | English |
| last_indexed | 2024-12-16T15:42:30Z |
| publishDate | 2021-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Mathematics |
| spelling | doaj.art-841e5f46840a4d42af4dc2ecfb8568462022-12-21T22:25:57ZengAIMS PressAIMS Mathematics2473-69882021-01-01632196221610.3934/math.2021133Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19Abdullah Ali H. Ahmadini0Amal S. Hassan1Ahmed N. Zaky 2Shokrya S. Alshqaq 31. Department of Mathematics, Faculty of Science, Jazan University, Jazan, Saudi Arabia2. Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt2. Faculty of Graduate Studies for Statistical Research, Cairo University, Egypt 3. Institute of National Planning, Egypt4. Department of Mathematics, Faculty of Science, Jazan University, Jazan, Saudi ArabiaDynamic cumulative residual entropy is a recent measure of uncertainty which plays a substantial role in reliability and survival studies. This article comes up with Bayesian estimation of the dynamic cumulative residual entropy of Pareto Ⅱ distribution in case of non-informative and informative priors. The Bayesian estimator and the corresponding credible interval are obtained under squared error, linear exponential (LINEX) and precautionary loss functions. The Metropolis-Hastings algorithm is employed to generate Markov chain Monte Carlo samples from the posterior distribution. A simulation study is done to implement and compare the accuracy of considered estimates in terms of their relative absolute bias, estimated risk and the width of credible intervals. Regarding the outputs of simulation study, Bayesian estimate of dynamic cumulative residual entropy under LINEX loss function is preferable than the other estimates in most of situations. Further, the estimated risks of dynamic cumulative residual entropy decrease as the value of estimated entropy decreases. Eventually, inferential procedure developed in this paper is illustrated via a real data.http://www.aimspress.com/article/doi/10.3934/math.2021133?viewType=HTMLshannon entropydynamic cumulative residual entropypareto ⅱ distributionbayesian estimatorsloss functions |
| spellingShingle | Abdullah Ali H. Ahmadini Amal S. Hassan Ahmed N. Zaky Shokrya S. Alshqaq Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19 AIMS Mathematics shannon entropy dynamic cumulative residual entropy pareto ⅱ distribution bayesian estimators loss functions |
| title | Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19 |
| title_full | Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19 |
| title_fullStr | Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19 |
| title_full_unstemmed | Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19 |
| title_short | Bayesian inference of dynamic cumulative residual entropy from Pareto Ⅱ distribution with application to COVID-19 |
| title_sort | bayesian inference of dynamic cumulative residual entropy from pareto ii distribution with application to covid 19 |
| topic | shannon entropy dynamic cumulative residual entropy pareto ⅱ distribution bayesian estimators loss functions |
| url | http://www.aimspress.com/article/doi/10.3934/math.2021133?viewType=HTML |
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