Modified beta modified-Weibull distribution

Saboor, Abdus; Khan, Muhammad Nauman; Cordeiro, Gauss M.; Pascoa, Marcelino A. R.; Bortolini, Juliano; Mubeen, Shahid

doi:10.1007/s00180-018-0822-y

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The Bayes estimates were obtained using MCMC and LA methods. Saboor et al 13 discussed the estimation for parameters of modified beta modified Weibull distribution using MLE and Bayes methods. Shahzad et al 14 considered the estimation of beta exponentiated modified Weibull distribution using MLE.…”

Section: Introductionmentioning

confidence: 99%

Applicability of modified weibull extension distribution in modeling censored medical datasets: a bayesian perspective

Feroze

Tahir

Noor‐ul‐Amin

et al. 2022

Sci Rep

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There are some contributions analyzing the censored medical datasets using modifications of the conventional lifetime distribution; however most of the said contributions did not considered the modification of the Weibull distribution (WD). The WD is an important lifetime model. Due to its prime importance in modeling life data, many researchers have proposed different modifications of WD. One of the most recent modifications of WD is Modified Weibull Extension distribution (MWED). However, the ability of MWED to model the censored medical data has not yet been explored in the literature. We have explored the suitability of the model in modeling censored medical datasets. The analysis has been carried out using Bayesian methods under different loss functions and informative priors. The approximate Bayes estimates have been computed using Lindley’s approximation. Based on detailed simulation study and real life analysis, it has been concluded that Bayesian methods performed better as compared to maximum likelihood estimates. In case of small samples, the performance of Bayes estimates under ELF and informative prior was the best. However, in case of large samples, the choice of prior and loss function did not affect the efficiency of the results to a large extend. The MWED performed efficiently in modeling real censored datasets relating to survival times of the leukemia and bile duct cancer patients. The MWED was explored to be a very promising candidate model for modeling censored medical datasets.

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Section: Introductionmentioning

confidence: 99%

Applicability of modified weibull extension distribution in modeling censored medical datasets: a bayesian perspective

Feroze

Tahir

Noor‐ul‐Amin

et al. 2022

Sci Rep

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show abstract

“…where r = θH(x;σ) The Modified Beta G has not been extensively used in obtaining generalized distributions. Some of its submodels in literature are Modified Beta Gompertz ( [12]), Modified Beta Modified Weibull ( [25]) e.t.c. Therefore, in this research, we are combining both the modified beta-G family of distributions and the Weibull-H family of distributions to obtain a new family of distributions that is more flexible and has the potentiality to fit data better than the modified beta-G and the Weibull-G family of distributions.…”

Section: Introductionmentioning

confidence: 99%

Statistical properties and applications of the modified beta weibull family of distributions to engineering data

Awodutire,

Sule

2024

IFS

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In this work, a new family of distribution, which generalizes the Beta Weibull-G family by the introduction of a shape parameter to enhance better fit and flexibility, called the Modified Beta Weibull-G family of distributions is obtained. The mixture representation of the derived family of distributions was discussed, with the results effective in studying moments, moment generating functions, order statistics. Parameters of the family of distributions were estimated using the maximum likelihood estimation method. By utilizing this modified class of distributions, we build a new distribution called the modified beta Weibull Weibull and applied it to engineering datasets. Application revealed a better performance in model fit, compared to some other distributions.

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“…Nofal et al 11 proposed transmuted exponentiated additive WD and estimated the concerned model parameters using classical and Bayesian methods. Similarly, the MLE and Bayesian estimation was considered for burr XII exponential distribution, 12 modified beta modified WD, 13 beta exponentiated modified Weibull distribution, 14 inverse WD, 15 exponentiated inverse Rayleigh distribution, 16 and transmuted exponentiated modified Weibull distribution. 17 The other important contributions regarding Bayesian analysis of the other models are available in the following contributions.…”

Section: Introductionmentioning

confidence: 99%

On suitability of modified Weibull extension distribution in modeling product lifetimes and reliability

Alshenawy

Feroze

Tahir

et al. 2022

Advances in Mechanical Engineering

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The Weibull distribution (WD) is an important lifetime model. Due to its prime importance in modeling life data, many researchers have proposed different modifications of WD. One of the most recent modifications of WD is Modified Weibull Extension distribution (MWEM). The MWEM has been shown better in modeling lifetime data as compared to WD. However it comparison with other modifications of WD, in modeling product lifetimes and reliability, is missing in the current literature. We have attempted to bridge up this gap. The Bayesian methods have been proposed for the analysis under non-informative (uniform) and informative (gamma) priors. Since the Bayes estimates for the model parameters were not available in closed form, the Lindley’s approximation (LA) has been used for numerical solutions. Based on detailed simulation study and real life analysis, it has been concluded that Bayesian methods performed better as compared to maximum likelihood estimates (MLE) in estimating the model parameters. The MWEM performed better than eighteen other modifications of WD in modeling real datasets regarding electric and mechanical components. The reliability and entropy estimation for the said datasets has also been discussed. The estimates for parameters of MWEM were quite consistent in nature. In case of small samples, the performance of Bayes estimates under ELF and informative prior was the best. However, in case of large samples, the choice of prior and loss function did not affect the efficiency of the results to a large extend.

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Modified beta modified-Weibull distribution

Cited by 9 publications

References 17 publications

Applicability of modified weibull extension distribution in modeling censored medical datasets: a bayesian perspective

Applicability of modified weibull extension distribution in modeling censored medical datasets: a bayesian perspective

Statistical properties and applications of the modified beta weibull family of distributions to engineering data

On suitability of modified Weibull extension distribution in modeling product lifetimes and reliability

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