Exponentiated modified Weibull extension distribution

Sarhan, Ammar M.; Apaloo, Joseph

doi:10.1016/j.ress.2012.10.013

Cited by 99 publications

(89 citation statements)

References 17 publications

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“…The other fitted models are: the additive Weibull (AddW) [54], modified-Weibull (MW) [30], Sarhan-Zaindin modified Weibull (SZMW) [48] and beta-modified Weibull (BMW) [50]. Their associated densities are given by:…”

Section: Data Set 2: Device Failure Times Datamentioning

confidence: 99%

The Weibull-Power Function Distribution With Applications

Tahir¹,

Alizadeh²,

Mansoor³

et al. 2014

HJMS

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Recently, several attempts have been made to define new models that extend well-known distributions and at the same time provide great flexibility in modelling real data. We propose a new four-parameter model named the Weibull-power function (WPF) distribution which exhibits bathtub-shaped hazard rate. Some of its statistical properties are obtained including ordinary and incomplete moments, quantile and generating functions, Rényi and Shannon entropies, reliability and order statistics. The model parameters are estimated by the method of maximum likelihood. A bivariate extension is also proposed. The new distribution can be implemented easily using statistical software packages. We investigate the potential usefulness of the proposed model by means of two real data sets. In fact, the new model provides a better fit to these data than the additive Weibull, modified Weibull, Sarahan-Zaindin modified Weibull and beta-modified Weibull distributions, suggesting that it is a reasonable candidate for modeling survival data.

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Section: Data Set 2: Device Failure Times Datamentioning

confidence: 99%

The Weibull-Power Function Distribution With Applications

Tahir¹,

Alizadeh²,

Mansoor³

et al. 2014

HJMS

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“…Due to practical utility in bio and reliability disciplines, numerous generalizations of Weibull distribution have been proposed in the literature aiming to improve its characteristics and to model real world scenario with nonmonotonic failure rate functions. These generalizations, including a statistical model with bathtub failure rate studied by Xie and Lai [20], Sarhan and Zaindin [17], Beta-Weibull (BW) distribution of Famoye et al [11], Kumaraswamy Weibull (KW) distribution proposed by Cordeiro et al [10], Generalized modified Weibull (GMW) distribution proposed by Carrasco et al [9], Exponentiated modified Weibull extension (EMWEx) distribution introduced by Sarhan and Apaloo [16], Flexible Weibull (FWEx) distribution of Bebbington et al [8], Generalized Flexible Weibull Extension (GFWEx) distribution studied by Ahmad and Iqbal [1], other extensions of Weibull model proposed by are [2], [3], [4], [5] and [6], respectively.…”

Section: Introductionmentioning

confidence: 99%

The New Extended Flexible Weibull Distribution and Its Applications

Ahmad¹

2017

IJDSA

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Abstract:The present article considers a new function to propose a new lifetime distribution. The new distribution is introduced by mixing up a linear system of the two logarithms of cumulative hazard functions. The proposed model is called new extended flexible Weibull distribution and is able to model lifetime with bathtub shaped failure rates and offers greater flexibility. Therefore, it can be quite valuable to use an alternative model to other existing lifetime distributions, where, modeling of real data sets with bathtub shaped failure rates are of interest. A brief description of the statistical properties along with estimation of the parameters through maximum likelihood procedure are discussed. The potentiality of the proposed model is showed by discussing two real data sets. For these data sets, the proposed model outclasses the Flexible Weibull Extension, Inverse Flexible Weibull Extension and Modified Weibull distributions.

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“…Some generalization of the Weibull distribution studied in the literature includes, but are not limited to, exponentiated Weibull (Mudholkar & Srivastava, 1993;Mudholkar, Srivastava, & Freimer, 1995;Mudholkar, Srivastava, & Kollia, 1996), additive Weibull (Xie & Lai, 1995), Marshall-Olkin extended Weibull (Ghitany, Al-Hussaini, & Al-Jarallah, 2005), beta Weibull (Famoye, Lee, & Olumolade, 2005), modified Weibull (Sarhan & Zaindin, 2009), beta modified Weibull (Silva, Ortega, & Cordeiro, 2010), transmuted Weibull (Aryal & Tsokos, 2011), extended Weibull (Xie, Tang, & Goh, 2002), modified Weibull (Lai, Xie, & Murthy, 2003), Kumaraswamy Weibull (Cordeiro, Ortega, & Nadarajah, 2010), Kumaraswamy modified Weibull (Cordeiro, Ortega, & Silva, 2012), Kumaraswamy inverse Weibull (Shahbaz, Shazbaz, & Butt, 2012), exponentiated generalized Weibull (Cordeiro, Ortega, & Cunha 2013), McDonald modified Weibull (Merovci & Elbatal, 2013), beta inverse Weibull (Hanook, Shahbaz, Mohsin,& Kibria, 2013), transmuted exponentiated generalized Weibull , McDonald Weibull (Cordeiro, Hashimoto, & Ortega, 2014), gamma Weibull (Provost, Saboor, & Ahmad, 2011), transmuted modified Weibull (Khan & King, 2013), beta Weibull (Lee, Famoye, & Olumolade, 2007), generalized transmuted Weibull (Nofal, Afify, Yousof, & Cordeiro, 2015), transmuted additive Weibull (Elbatal & Aryal, 2013), exponentiated generalized modified Weibull (Aryal & Elbatl, 2015), transmuted exponentiated additive Weibull , Marshall Olkin additive Weibull (Afify, Cordeiro, Yousof, Saboor, & Ortega, 2016) and Kumaraswamy transmuted exponentiated additive Weibull (Nofal, Afify, Yousof, Granzotto,& Louzada, 2016) distributions.…”

Section: Introductionmentioning

confidence: 99%

The Topp-Leone Generated Weibull Distribution: Regression Model, Characterizations and Applications

Aryal¹,

Ortega²,

Hamedani³

et al. 2016

IJSP

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This paper introduces a new four-parameter lifetime model called the Topp Leone Generated Weibull (TLGW) distribution. This distribution is a generalization of the two parameter Weibull distribution using the genesis of Topp-Leone distribution. We derive many of its structural properties including ordinary and incomplete moments, quantile and generating functions and order statistics. Parameter estimation using maximum likelihood method and simulation results to assess effectiveness of the distribution are discussed. Also, for the first time, we introduce a regression model based on the new distribution. We prove empirically the importance and flexibility of the new model in modeling various types of real data sets.

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Exponentiated modified Weibull extension distribution

Cited by 99 publications

References 17 publications

The Weibull-Power Function Distribution With Applications

The Weibull-Power Function Distribution With Applications

The New Extended Flexible Weibull Distribution and Its Applications

The Topp-Leone Generated Weibull Distribution: Regression Model, Characterizations and Applications

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