Extended inverse Weibull distribution with reliability application

Okasha, Hassan M.; El-Baz, A. H.; Tarabia, A. M. K.; Basheer, Abdulkareem M.

doi:10.1016/j.joems.2017.02.006

Cited by 38 publications

(23 citation statements)

References 9 publications

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“…The distribution which in (4) has special cases as the following (i) If , then, the MOGIW distribution reduces to the Marshall-Olkin extended inverse Weibull distribution which studied by (Okasha et al 2017) The survival function R x and the hazard rate function h x of the MOGIW distribution respectively are…”

Section: New Model and Its Propertiesmentioning

confidence: 99%

The Marshall–Olkin Generalized Inverse Weibull Distribution: Properties and Application

Salem¹

2019

MAS

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In this paper, a new distribution namely, The Marshall–OlkinGeneralized Inverse Weibull Distribution is illustrated and studied. The new distribution is very flexible and contains sub-models such asinverse exponential, inverse Rayleigh, Weibull, inverse Weibull, Marshall–Olkininverse Weibull and Fréchetdistributions. Also, the hazard function of the new distribution can produce variety of forms:an increase, a decrease and an upside-down bathtub. Some properties such as hazard function, quintile function, entropy, moment generating function and order statistics are obtained. Different estimation approaches namely, maximum likelihood estimators, interval estimators, least square estimators, fisher information matrix and asymptotic confidence intervals are described. To illustrate the superior performance of the proposed distribution, a simulation study and a real data analysis are investigated against other models.

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Section: New Model and Its Propertiesmentioning

confidence: 99%

The Marshall–Olkin Generalized Inverse Weibull Distribution: Properties and Application

Salem¹

2019

MAS

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“…Step(1): A random sample 1 ,..., n XX of sizes n= (10,20,30,50,100) and i u are random sample from uniform(0,1).…”

Section: Numerical Studymentioning

confidence: 99%

“…Rodrigues et al (2016) introduced exponentiated Kumaraswamy inverse Weibull distribution. Okasha et al (2017) introduced the Marshall-Olkin extended inverse Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

Odds Generalized Exponential-Inverse Weibull Distribution: Properties & Estimation

Hassan

El-Sherpieny

Mohamed

2018

Pak.j.stat.oper.res.

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Providing extended and generalized distribution is usually precious for many statisticians. A new distribution, called odds generalized exponential-inverse Weibull distribution (OGE-IW) is suggested for modeling lifetime data. Some structural properties of the new distribution are obtained. Three different estimation procedures, namely; maximum likelihood, percentiles and least squares, are used to estimate the model parameters of subject distribution. The consistency of the parameters of the OGE-IW distribution is demonstrated through a simulation study. A real data application is presented to illustrate the importance of the new distribution compared with some known distributions.

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“…Shafiei et al (2016) mention that IW distribution is an appropriate model for situation where hazard function is unimodal. The Marshall-Olkin IW distribution and its application in the context of reliability analysis is discussed in Okasha et al (2017). Hassan and Nassar (2018) studied properties and applications of IW generator of distributions.…”

Section: Introductionmentioning

confidence: 99%

A Generalization Of Inverse Marshall-Olkin Family Of Distributions

Jayakumar¹,

Sankaran²

2021

Journal of Data Science

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We introduce a new family of distributions namely inverse truncated discrete Linnik G family of distributions. This family is a generalization of inverse Marshall-Olkin family of distributions, inverse family of distributions generated through truncated negative binomial distribution and inverse family of distributions generated through truncated discrete Mittag-Leffler distribution. A particular member of the family, inverse truncated negative binomial Weibull distribution is studied in detail. The shape properties of the probability density function and hazard rate, model identifiability, moments, median, mean deviation, entropy, distribution of order statistics, stochastic ordering property, mean residual life function and stress-strength properties of the new generalized inverse Weibull distribution are studied. The unknown parameters of the distribution are estimated using maximum likelihood method, product spacing method and least square method. The existence and uniqueness of the maximum likelihood estimates are proved. Simulation is carried out to illustrate the performance of maximum likelihood estimates of model parameters. An AR(1) minification model with this distribution as marginal is developed. The inverse truncated negative binomial Weibull distribution is fitted to a real data set and it is shown that the distribution is more appropriate for modeling in comparison with some other competitive models.

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Extended inverse Weibull distribution with reliability application

Cited by 38 publications

References 9 publications

The Marshall–Olkin Generalized Inverse Weibull Distribution: Properties and Application

The Marshall–Olkin Generalized Inverse Weibull Distribution: Properties and Application

Odds Generalized Exponential-Inverse Weibull Distribution: Properties & Estimation

A Generalization Of Inverse Marshall-Olkin Family Of Distributions

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