A new extended rayleigh distribution with applications of COVID-19 data
This paper aims to model the COVID-19 mortality rates in Italy, Mexico, and the Netherlands, by specifying an optimal statistical model to analyze the mortality rate of COVID-19. A new lifetime distribution with three-parameter is introduced by a combination of Rayleigh distribution and extended odd...
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| Published in | Results in physics Vol. 23; p. 104012 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Netherlands
Elsevier B.V
01.04.2021
The Authors. Published by Elsevier B.V Elsevier |
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| Abstract | This paper aims to model the COVID-19 mortality rates in Italy, Mexico, and the Netherlands, by specifying an optimal statistical model to analyze the mortality rate of COVID-19. A new lifetime distribution with three-parameter is introduced by a combination of Rayleigh distribution and extended odd Weibull family to produce the extended odd Weibull Rayleigh (EOWR) distribution. This new distribution has many excellent properties as simple linear representation, hazard rate function, and moment generating function. Maximum likelihood, maximum product spacing and Bayesian estimation methods are applied to estimate the unknown parameters of EOWR distribution. MCMC method is used for the Bayesian estimation. A numerical result of the Monte Carlo simulation is obtained to assess the use of estimation methods. Also, data analysis for the real data of mortality rate is considered. |
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| AbstractList | This paper aims to model the COVID-19 mortality rates in Italy, Mexico, and the Netherlands, by specifying an optimal statistical model to analyze the mortality rate of COVID-19. A new lifetime distribution with three-parameter is introduced by a combination of Rayleigh distribution and extended odd Weibull family to produce the extended odd Weibull Rayleigh (EOWR) distribution. This new distribution has many excellent properties as simple linear representation, hazard rate function, and moment generating function. Maximum likelihood, maximum product spacing and Bayesian estimation methods are applied to estimate the unknown parameters of EOWR distribution. MCMC method is used for the Bayesian estimation. A numerical result of the Monte Carlo simulation is obtained to assess the use of estimation methods. Also, data analysis for the real data of mortality rate is considered. This paper aims to model the COVID-19 mortality rates in Italy, Mexico, and the Netherlands, by specifying an optimal statistical model to analyze the mortality rate of COVID-19. A new lifetime distribution with three-parameter is introduced by a combination of Rayleigh distribution and extended odd Weibull family to produce the extended odd Weibull Rayleigh (EOWR) distribution. This new distribution has many excellent properties as simple linear representation, hazard rate function, and moment generating function. Maximum likelihood, maximum product spacing and Bayesian estimation methods are applied to estimate the unknown parameters of EOWR distribution. MCMC method is used for the Bayesian estimation. A numerical result of the Monte Carlo simulation is obtained to assess the use of estimation methods. Also, data analysis for the real data of mortality rate is considered.This paper aims to model the COVID-19 mortality rates in Italy, Mexico, and the Netherlands, by specifying an optimal statistical model to analyze the mortality rate of COVID-19. A new lifetime distribution with three-parameter is introduced by a combination of Rayleigh distribution and extended odd Weibull family to produce the extended odd Weibull Rayleigh (EOWR) distribution. This new distribution has many excellent properties as simple linear representation, hazard rate function, and moment generating function. Maximum likelihood, maximum product spacing and Bayesian estimation methods are applied to estimate the unknown parameters of EOWR distribution. MCMC method is used for the Bayesian estimation. A numerical result of the Monte Carlo simulation is obtained to assess the use of estimation methods. Also, data analysis for the real data of mortality rate is considered. |
| ArticleNumber | 104012 |
| Author | Aljohani, Hassan M. Almongy, Hisham M. Alghamdi, Abdulaziz S. Hafez, E.H. Almetwally, Ehab M. |
| Author_xml | – sequence: 1 givenname: Hisham M. surname: Almongy fullname: Almongy, Hisham M. organization: Department of Statistics, Faculty of Commerce, Mansoura University, Mansoura, Egypt – sequence: 2 givenname: Ehab M. surname: Almetwally fullname: Almetwally, Ehab M. organization: Department of Statistics, Faculty of Business Administration, Delta University of Science and Technology, Egypt – sequence: 3 givenname: Hassan M. surname: Aljohani fullname: Aljohani, Hassan M. email: hmjohani@tu.edu.sa organization: Department of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia – sequence: 4 givenname: Abdulaziz S. surname: Alghamdi fullname: Alghamdi, Abdulaziz S. organization: Department of Mathematics, College of Science & Arts, King Abdulaziz University, P. O. Box 344, Rabigh 21911, Saudi Arabia – sequence: 5 givenname: E.H. surname: Hafez fullname: Hafez, E.H. email: eslamhussam@science.helwan.edu.eg organization: Mathematics Department, Faculty of Science, Helwan University, Helwan, Egypt |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33728260$$D View this record in MEDLINE/PubMed |
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| Keywords | Extended odd Weibull family COVID-19 Bayesian 62F10 Rayleigh distribution Maximum product spacing 60E05 |
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| Title | A new extended rayleigh distribution with applications of COVID-19 data |
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