Marshall–Olkin gamma–Weibull distribution with applications

Saboor, Abdus; Pogány, Tibor K.

doi:10.1080/03610926.2014.953694

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…This data set can also be found in Nadarajah and Kotz [20]. While, the second data set taken from Saboor and Pogány [24]. We fit a special model (called NAPTW distribution) of the proposed family in comparison with five other well-known competing distributions.…”

Section: Applicationsmentioning

confidence: 99%

A new alpha power transformed family of distributions: properties and applications to the Weibull model

Elbatal¹,

Ahmad²,

Elgarhy³

et al. 2018

J. Nonlinear Sci. Appl.

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In this article, a new technique of alpha-power transformation is used to propose a new class of lifetime distributions. Four special models of the new family are presented. Some mathematical properties of the proposed model including estimation of the unknown parameters using the method of maximum likelihood are discussed. For the illustrative purposes of the new proposal, a three-parameter special model of this class, namely, new alpha-power transformed Weibull distribution is considered in detail. The proposed distribution offers greater distributional flexibility and is able to model data with increasing, decreasing, and constant or more importantly with bathtub-shaped failure rates. Type-1 and Type-II censoring estimation are discussed. A simulation study based on complete sample of the new model is also carried out. Finally, the usefulness and efficiency of the new proposal is illustrated by analyzing two real data sets.

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Section: Applicationsmentioning

confidence: 99%

A new alpha power transformed family of distributions: properties and applications to the Weibull model

Elbatal¹,

Ahmad²,

Elgarhy³

et al. 2018

J. Nonlinear Sci. Appl.

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“…where G(x, ψ) is the baseline cumulative distribution function(cdf) which may depend on the vector parameter ψ. Many famous MO-G families and its special distributions are available in literature such as Marshall-Olkin-G (Marshall and Olkin;1997), the MO extended Lomax (Ghitany et al;2007), MO semi-Burr and MO Burr (Jayakumar and Mathew;2008), MO q-Weibull (Jose et al;2010), MO extended Lindley (Ghitany et al;2012), the generalized MO-G (Nadarajah et al (2013), the MO Fréchet (Krishna et al;, the MO family (Cordeiro and Lemonte;, MO extended Weibull (Santos-Neto et al;, the beta MO-G (Alizadeh et al 2015), the MO generalized exponential (Ristić, & Kundu;, MO gamma-Weibull (Saboor and Pogány;, MO generalized-G (Yousof et al;, MO additive Weibull (Afify et al; and Weibull MO family (Korkmaz et al;2019). This paper is sketched into the following sections.…”

Section: Introductionmentioning

confidence: 99%

On Burr III Marshal Olkin family: development, properties, characterizations and applications

et al. 2019

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In this paper, a flexible family of distributions with unimodel, bimodal, increasing, increasing and decreasing, inverted bathtub and modified bathtub hazard rate called Burr III-Marshal Olkin-G (BIIIMO-G) family is developed on the basis of the T-X family technique. The density function of the BIIIMO-G family is arc, exponential, left-skewed, right-skewed and symmetrical shaped. Descriptive measures such as quantiles, moments, incomplete moments, inequality measures and reliability measures are theoretically established. The BIIIMO-G family is characterized via different techniques. Parameters of the BIIIMO-G family are estimated using maximum likelihood method. A simulation study is performed to illustrate the performance of the maximum likelihood estimates (MLEs). The potentiality of BIIIMO-G family is demonstrated by its application to real data sets.

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“…Extensions of the Weibull distribution arise in different areas of research as is often pointed out in the literature, see for instance [2] and the references therein. Various extended Weibull models have an upsidedown bathtub shaped hazard rate, which is the case for the extensions discussed by [11] and [20], among others.…”

Section: Introductionmentioning

confidence: 99%

The Marshall-Olkin Exponential Weibull Distribution

Saboor¹,

Provost²,

Pogány³

2015

HJMS

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A new four-parameter model called the Marshall-Olkin exponential-Weibull probability distribution is being introduced in this paper, generalizing a number of known lifetime distributions. This model turns out to be quite flexible for analyzing positive data. The hazard rate functions of new model can be increasing and bathtub shaped. Our main objectives are to obtain representations of certain associated statistical functions, to estimate the parameters of the proposed distribution and to discuss its modality. As an application, the probability density function is utilized to model two actual data sets. The new distribution is shown to provide a better fit than related distributions as measured by the Anderson-Darling and Cramér-von Mises goodnessof-fit statistics. The proposed distribution may serve as a viable alternative to other distributions available in the literature for modeling positive data arising in various fields of scientific investigation such as reliability theory, hydrology, medicine, meteorology, survival analysis and engineering.

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Marshall–Olkin gamma–Weibull distribution with applications

Cited by 16 publications

References 16 publications

A new alpha power transformed family of distributions: properties and applications to the Weibull model

A new alpha power transformed family of distributions: properties and applications to the Weibull model

On Burr III Marshal Olkin family: development, properties, characterizations and applications

The Marshall-Olkin Exponential Weibull Distribution

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