A new versatile modification of the Rayleigh distribution for modeling COVID-19 mortality rates

In several experiments of survival analysis, the cause of death or failure of any subject may be characterized by more than one cause. Since the cause of failure may be dependent or independent, in this work, we discuss the competing risk lifetime model under progressive type-II censored where the removal follows a binomial distribution. We consider the Akshaya lifetime failure model under independent causes and the number of subjects removed at every failure time when the removal follows the binomial distribution with known parameters. The classical and Bayesian approaches are used to account for the point and interval estimation procedures for parameters and parametric functions. The Bayes estimate is obtained by using the Markov Chain Monte Carlo (MCMC) method under symmetric and asymmetric loss functions. We apply the Metropolis–Hasting algorithm to generate MCMC samples from the posterior density function. A simulated data set is applied to diagnose the performance of the two techniques applied here. The data represented the survival times of mice kept in a conventional germ-free environment, all of which were exposed to a fixed dose of radiation at the age of 5 to 6 weeks, which was used as a practice for the model discussed. There are 3 causes of death. In group 1, we considered thymic lymphoma to be the first cause and other causes to be the second. On the base of mice data, the survival mean time (cumulative incidence function) of mice of the second cause is higher than the first cause.

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“…Such clinical issues must be addressed during the design phase before conducting the statistical analysis. For more reading, see Abushal [61,62].…”

Section: Discussionmentioning

confidence: 99%

Estimation for Akshaya Failure Model with Competing Risks under Progressive Censoring Scheme with Analyzing of Thymic Lymphoma of Mice Application

Abushal

Kumar²,

Muse³

et al. 2022

Complexity

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“…12 ), we deduce that the TLKMOIEx model is a better fit for the three datasets than the EMIEEx, TIR, IWIEx, AIW, LIEx and IEx models. For more reading about distributions and statistical inferences and modeling see [42] , [43] , [44] , [45] , [46] , [47] .…”

Section: Real Data Explorationmentioning

confidence: 99%

A new Topp-Leone Kumaraswamy Marshall-Olkin generated family of distributions with applications

Atchadé,

N'bouké,

Djibril

et al. 2024

Heliyon

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“…The Inverse Weibull Inverse Exponential distribution was proposed by 6 , the Alpha Power Exponentiated Inverse Rayleigh distribution by 7 , the modified Rayleigh distribution for modeling COVID-19 mortality rates by 8 , the Kumaraswamy-Gull Alpha Power Rayleigh distribution with it's properties and application to HIV/AIDS data by 9 , the Gull-Alpha Power Weibull distribution with applications to real and simulated data by 10 , the Generalized Exponential distribution by 11 , 12 , and 13 , the two-parameter Inverse Exponential distribution with a decreasing failure rate by 14 , a new family of generalized distributions based on Alpha Power Transformation with application to cancer data by 15 , on the Exponentiated Generalized Exponentiated Exponential distribution with Properties and Application by 16 , the Exponentiated Generalized Class of distributions by 17 and many more.…”

Section: Introductionmentioning

confidence: 99%

The Generalized Alpha Power Exponentiated Inverse Exponential distribution and it's application to real data

Kargbo,

Waititu,

Wanjoya

2023

Preprint

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This paper proposes a new distribution named “The Generalized Alpha Power Exponentiated Inverse Exponential (GAPEIEx for short) distribution” with four parameters, from which one (1) scale and three (3) shape parameters and the statistical properties such as Survival function, Hazard function, Quantile function, r^(th) Moment, Rényi Entropy, and Order Statistics of the new distribution are derived. The method of maximum likelihood estimation (MLE) is used to estimate the parameters of the distribution. The performance of the estimators is assessed through simulation, which shows that the maximum likelihood method works well in estimating the parameters. The GAPEIEx distribution was applied to simulated and real data in order to access the flexibility and adaptability of the distribution, and it happens to perform better than its submodels.

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A new versatile modification of the Rayleigh distribution for modeling COVID-19 mortality rates

Cited by 10 publications

References 20 publications

Estimation for Akshaya Failure Model with Competing Risks under Progressive Censoring Scheme with Analyzing of Thymic Lymphoma of Mice Application

Estimation for Akshaya Failure Model with Competing Risks under Progressive Censoring Scheme with Analyzing of Thymic Lymphoma of Mice Application

A new Topp-Leone Kumaraswamy Marshall-Olkin generated family of distributions with applications

The Generalized Alpha Power Exponentiated Inverse Exponential distribution and it's application to real data

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