A new extended rayleigh distribution with applications of COVID-19 data

Hisham M. Almongy; Ehab M. Almetwally; Hassan M. Aljohani; Abdulaziz S. Alghamdi; E. H. Hafez

doi:10.1016/j.rinp.2021.104012

A new extended rayleigh distribution with applications of COVID-19 data

Hisham M. Almongy, Ehab M. Almetwally, Hassan M. Aljohani, Abdulaziz S. Alghamdi, E. H. Hafez

Research output: Contribution to journal › Article › peer-review

107 Scopus citations

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.

Original language	English
Article number	104012
Journal	Results in Physics
Volume	23
DOIs	https://doi.org/10.1016/j.rinp.2021.104012
State	Published - Apr 2021
Externally published	Yes

Keywords

Bayesian
COVID-19
Extended odd Weibull family
Maximum product spacing
Rayleigh distribution

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.rinp.2021.104012

Cite this

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title = "A new extended rayleigh distribution with applications of COVID-19 data",

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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AU - Almongy, Hisham M.

AU - Almetwally, Ehab M.

AU - Aljohani, Hassan M.

AU - Alghamdi, Abdulaziz S.

AU - Hafez, E. H.

PY - 2021/4

Y1 - 2021/4

N2 - 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.

AB - 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.

KW - Bayesian

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KW - Extended odd Weibull family

KW - Maximum product spacing

KW - Rayleigh distribution

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A new extended rayleigh distribution with applications of COVID-19 data

Abstract

Keywords

UN SDGs

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