On the analysis of number of deaths due to Covid −19 outbreak data using a new class of distributions
In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter,...
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| Published in | Results in physics Vol. 21; p. 103747 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.02.2021
The Authors. Published by Elsevier B.V Elsevier |
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| Abstract | In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter, every existing lifetime model can be made more flexible with suggested development. Some specific statistical attributes of the GLET-GTII are investigated, such as quantiles, uncertainty measures, survival function, moments, reliability, and hazard function etc. We describe two methods of parametric estimations of GLET-GTII discussed by using maximum likelihood estimators and Bayesian paradigm. The Monte Carlo simulation analysis shows that estimators are consistent. Two real life implementations are performed to scrutinize the suitability of our current strategy. These real life data is related to Infectious diseases (COVID-19). These applications identify that by using the current approach, our proposed model outperforms than other well known existing models available in the literature. |
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| AbstractList | In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter, every existing lifetime model can be made more flexible with suggested development. Some specific statistical attributes of the GLET-GTII are investigated, such as quantiles, uncertainty measures, survival function, moments, reliability, and hazard function etc. We describe two methods of parametric estimations of GLET-GTII discussed by using maximum likelihood estimators and Bayesian paradigm. The Monte Carlo simulation analysis shows that estimators are consistent. Two real life implementations are performed to scrutinize the suitability of our current strategy. These real life data is related to Infectious diseases (COVID-19). These applications identify that by using the current approach, our proposed model outperforms than other well known existing models available in the literature. In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter, every existing lifetime model can be made more flexible with suggested development. Some specific statistical attributes of the GLET-GTII are investigated, such as quantiles, uncertainty measures, survival function, moments, reliability, and hazard function etc. We describe two methods of parametric estimations of GLET-GTII discussed by using maximum likelihood estimators and Bayesian paradigm. The Monte Carlo simulation analysis shows that estimators are consistent. Two real life implementations are performed to scrutinize the suitability of our current strategy. These real life data is related to Infectious diseases (COVID-19). These applications identify that by using the current approach, our proposed model outperforms than other well known existing models available in the literature.In this article, we develop a generator to suggest a generalization of the Gumbel type-II model known as generalized log-exponential transformation of Gumbel Type-II (GLET-GTII), which extends a more flexible model for modeling life data. Owing to basic transformation containing an extra parameter, every existing lifetime model can be made more flexible with suggested development. Some specific statistical attributes of the GLET-GTII are investigated, such as quantiles, uncertainty measures, survival function, moments, reliability, and hazard function etc. We describe two methods of parametric estimations of GLET-GTII discussed by using maximum likelihood estimators and Bayesian paradigm. The Monte Carlo simulation analysis shows that estimators are consistent. Two real life implementations are performed to scrutinize the suitability of our current strategy. These real life data is related to Infectious diseases (COVID-19). These applications identify that by using the current approach, our proposed model outperforms than other well known existing models available in the literature. |
| ArticleNumber | 103747 |
| Author | Al-Mdallal, Qasem M. Sindhu, Tabassum Naz Shafiq, Anum |
| Author_xml | – sequence: 1 givenname: Tabassum Naz surname: Sindhu fullname: Sindhu, Tabassum Naz organization: Department of Statistics, Quaid-i-Azam University 45320, Islamabad 44000, Pakistan – sequence: 2 givenname: Anum surname: Shafiq fullname: Shafiq, Anum organization: School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China – sequence: 3 givenname: Qasem M. surname: Al-Mdallal fullname: Al-Mdallal, Qasem M. email: q.almdallal@uaeu.ac.ae organization: Department of Mathematical Sciences, UAE University, P.O. Box 15551, Al-Ain, United Arab Emirates |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33520628$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.stamet.2008.12.003 10.22237/jmasm/1478002080 10.1016/S0140-6736(20)30183-5 10.1016/j.aej.2020.09.060 10.1093/biomet/84.3.641 10.1007/s40745-020-00309-6 10.1007/s10985-010-9161-1 10.1080/03610929808832134 10.18187/pjsor.v13i1.1461 10.3390/e15104011 10.1080/00224065.1995.11979578 10.1371/journal.pone.0231908 10.1214/aoms/1177731747 10.1177/0142331215578690 10.1016/S0140-6736(20)30185-9 10.1007/s00362-009-0271-3 10.1081/STA-120003130 10.1080/00949650903530745 |
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| Keywords | Entropies Gumbel type-II model Generalized log-exponential distribution Bayesian analysis Stochastic order |
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| Title | On the analysis of number of deaths due to Covid −19 outbreak data using a new class of distributions |
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