The Weibull–Dagum distribution: Properties and applications

Tahir, M. H.; Cordeiro, Gauss M.; Mansoor, Muhammad; Zubair, Muhammad; Alizadeh, Morad

doi:10.1080/03610926.2014.983610

Cited by 19 publications

(14 citation statements)

References 38 publications

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“…Let Z be a random variable having the Dagum distribution (3) with parameters a, b and c. For r ≤ b, the rth ordinary and incomplete moments of Z are given by Tahir et al (2016) …”

Section: Linear Representationmentioning

confidence: 99%

“…Oluyede & Rajasooriya (2013) proposed the six paprameter Mc-Dagum Distribution. Among the various transformations found in the literature, we remember the Exponentiated Kumaraswamy Dagum distribution (Huang & Oluyede 2014), the Weighted Dagum (Oluyede & Ye 2014), the transmuted Dagum distribution (Elbatal & Aryal 2015), the Weibull Dagum distribution (Tahir, Cordeiro, Mansoor, Zubair & Alizadeh 2016), the Dagum Poisson Distribution (Oluyede, Motsewabagale, Huang, Warahena-Liyanage & Pararai 2016).…”

Section: Introductionmentioning

confidence: 99%

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The Odd Log-Logistic Dagum Distribution: Properties and Applications

Domma¹,

Eftekharian²,

Afify³

et al. 2018

Rev. colomb. estad.

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This paper introduces a new four-parameter lifetime model called the odd log-logistic Dagum distribution. The new model has the advantage of being capable of modeling various shapes of aging and failure criteria. We derive some structural properties of the model odd log-logistic Dagum such as order statistics and incomplete moments. The maximum likelihood method is used to estimate the model parameters. Simulation results to assess the performance of the maximum likelihood estimation are discussed. We prove empirically the importance and exibility of the new model in modeling real data. Este artículo introduce un nuevo modelo de sobrevida de cuatro paráme-tros llamado la distribución Odd Log-Logistica de Dagum. El nuevo modelo tiene la ventaja de ser capaz de modelar varias formas de envejecimiento y criterios de falla. Derivamos propiedades estructurales del modelo Odd Log-Logistica de Dagum tales como estadísticos de orden y momentos incompletos. El método de máxima verosimilitud es usado para estimar los parámetros del modelo. Se discuten resultados de algunas simulaciones, las cuales permitieron establecer la eciencia del método de máxima verosimilitud. Probamos empíricamente la importancia y exibilidad del nuevo modelo a través de un ejemplo en datos reales.Palabras clave: distribución Dagum; familia Odd-Log-Logística; máxima verosimilitud; estadísticas de orden.

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“…Let Z be a random variable having the Dagum distribution (3) with parameters a, b and c. For r ≤ b, the rth ordinary and incomplete moments of Z are given by Tahir et al (2016) …”

Section: Linear Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Odd Log-Logistic Dagum Distribution: Properties and Applications

Domma¹,

Eftekharian²,

Afify³

et al. 2018

Rev. colomb. estad.

Self Cite

View full text Add to dashboard Cite

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“…Many authors have used this family to propose an extension of some classical distributions. Tahir et al ([30], [31], [32]) defined the Weibull-Pareto, Weibull-Lomax and Weibull-Dagum distributions by taking G(t, θ) to be the cdf of the Pareto, Lomax and Dagum distributions, respectively. Afify et al [1] defined and studied the Weibull Burr XII distribution.…”

Section: Introductionmentioning

confidence: 99%

The Weibull Birnbaum-Saunders Distribution And Its Applications

Benkhelifa¹

2020

Stat., optim. inf. comput.

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A new lifetime model, with four positive parameters, called the Weibull Birnbaum-Saunders distribution is proposed. The proposed model extends the Birnbaum-Saunders distribution and provides great flexibility in modeling data in practice. Some mathematical properties of the new distribution are obtained including expansions for the cumulative and density functions, moments, generating function, mean deviations, order statistics and reliability. Estimation of the model parameters is carried out by the maximum likelihood estimation method. A simulation study is presented to show the performance of the maximum likelihood estimates of the model parameters. The flexibility of the new model is examined by applying it to two real data sets.

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“…As a result of this fact, researchers have developed many statistical families of distributions and study most of their properties. These include: The Transmuted Weibull lomax distribution by Afify et al, 1 kumaraswamy Marshal-olkin family by Afify et al, 2 Lomax generator by Cordeiro et al, 3 the weibull exponential by Oguntunde et al, 4 kumaraswamy-pareto by Bourguignon et al, 5 weibull-G family by Bourguignon et al, 6 the weibull-dagum distribution by Tahir et al, 7 the generalized transmuted-G family of distributions by Nofal et al, 8 among others. Recently, a lot of extensions of distributions have been proposed and studied based on the Weibull-G family of distributions by Bourguignon et al, 6 Among them is the Tahir, Merovci, Afify, Yousof, and Oguntunde et al [9][10][11][12]4 to mention but few.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2019

BBIJ

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In this paper, we introduced a four-parameter probability model called Weibull-Inverse Lomax distribution with decreasing, increasing and bathtub hazard rate function. The WIL distribution density function is J-shaped, positively skewed, and J-shaped in reverse. Some of the mentioned distribution's statistical characteristics are provided including moments, order statistics, entropy, mean, variance, moment generating function, and quantile function. The method of maximum likelihood estimation was used to estimate the parameters of the model. The distribution's importance is proved by its implementation to the bladder cancer data set. Goodness-of-fit of this distribution by various techniques demonstrates that the WIL distribution is empirically better for lifetime application.

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The Weibull–Dagum distribution: Properties and applications

Cited by 19 publications

References 38 publications

The Odd Log-Logistic Dagum Distribution: Properties and Applications

The Odd Log-Logistic Dagum Distribution: Properties and Applications

The Weibull Birnbaum-Saunders Distribution And Its Applications

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