A new four-parameter lifetime distribution

Nadarajah, Saralees; Shahsanaei, Fatemeh; Rezaei, Satar

doi:10.1080/00949655.2012.705284

Cited by 20 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance:The Marshall-Olkin Weibull distribution due to Marshall and Olkin (1997) becomes the RGD, when αgoodbreakafter=1goodbreakafter−p and λβgoodbreakafter=12σ2.The Marshall-Olkin Rayleigh distribution due to Ozel and Cakmakyapan (2015) is equivalent to the RGD, when γgoodbreakafter=1goodbreakafter−p.The Weibull-geometric distribution reduces to the RGD for αgoodbreakafter=2 and βgoodbreakafter=12σ (see Hamedani and Ahsanullah, 2011). The Weibull-geometric distribution (WGD) (Barreto-Souza et al, 2011) becomes the RGD, when αgoodbreakafter=2 and βgoodbreakafter=1σ12.The generalized linear failure rate-geometric (GLFRG) distribution due to Nadarajah et al (2014) boils down to the RGD for agoodbreakafter=0, αgoodbreakafter=1, and bgoodbreakafter=1σ2.…”

Section: Modelmentioning

confidence: 99%

See 1 more Smart Citation

On the Rayleigh-geometric distribution with applications

Okorie

Akpanta

Ohakwe

et al. 2019

Heliyon

View full text Add to dashboard Cite

A two-parameter Rayleigh-geometric distribution with increasing-decreasing-increasing and strictly increasing hazard rate characteristics is reviewed. Various properties are discussed and expressed analytically. The estimation of the distribution parameters is studied by the method of maximum likelihood and validated by a simulation study. Numerical examples based on two real data-sets on the waiting time in queue and CO 2 emissions are given. The Rayleigh-geometric distribution in this paper has a simpler analytical expression compared to the pre-existing distributions with different parameterizations.

show abstract

Section: Modelmentioning

confidence: 99%

“…The generalized linear failure rate-geometric (GLFRG) distribution due to Nadarajah et al (2014) boils down to the RGD for agoodbreakafter=0, αgoodbreakafter=1, and bgoodbreakafter=1σ2.…”

Section: Modelmentioning

confidence: 99%

On the Rayleigh-geometric distribution with applications

Okorie

Akpanta

Ohakwe

et al. 2019

Heliyon

View full text Add to dashboard Cite

show abstract

“…Recently, many studies have been done on GLFR distribution, and some authors have extended it: the generalized linear exponential (Mahmoud and Alam, 2010), beta-linear failure rate , Kumaraswamy-GLFR (Elbatal, 2013), modified-GLFR (Jamkhaneh, 2014), McDonald-GLFR (Elbatal et al, 2014), Poisson-GLFR (Cordeiro et al, 2015), GLFR-geometric (Nadarajah et al, 2014), and GLFR-power series (Alamatsaz and Shams, 2014) are some univariate extension of GLFR distribution. Kundu and Gupta (2011) proposed an extension of GE distribution that is a very flexible family of distribution.…”

Section: Introductionmentioning

confidence: 99%

An extension of the generalized linear failure rate distribution

Kazemi

Jafari

Tahmasebi

2016

Communications in Statistics - Theory and Methods

View full text Add to dashboard Cite

In this paper, we introduce a new extension of the generalized linear failure rate distributions. It includes some well-known lifetime distributions such as extension of generalized exponential and generalized linear failure rate distributions as special sub-models. In addition, it can have a constant, decreasing, increasing, upside-down bathtub (unimodal), and bathtub-shaped hazard rate function depending on its parameters. We provide some of its statistical properties such as moments, quantiles, skewness, kurtosis, hazard rate function, and reversible hazard rate function. The maximum likelihood estimation of the parameters is also discussed. At the end, a real data set is given to illustrate the usefulness of this new distribution in analyzing lifetime data.

show abstract

“…, then the cdf of the GLFRG model proposed by Nadarajah et al (2014a) can follow from the EGG class (Section 3.1) as…”

Section: Exponentiated-ll Geometric (Ellg) and Complementary Exponentmentioning

confidence: 99%

Compounding of distributions: a survey and new generalized classes

2016

View full text Add to dashboard Cite

Generalizing distributions is an old practice and has ever been considered as precious as many other practical problems in statistics. It simply started with defining different mathematical functional forms, and then induction of location, scale or inequality parameters. The generalization through induction of shape parameter(s) started in 1997, and the last two decades were full of such practices. But to cope with the complex situations under series and parallel structures, the art of mixing discrete and continuous started in 1998. In this article, we present a survey on compounding univariate distributions, their extensions and classes. We review several available compound classes and propose some new ones. The recent trends in the construction of generalized and compounding classes are discussed, and the need for future works are addressed.

show abstract

A new four-parameter lifetime distribution

Cited by 20 publications

References 38 publications

On the Rayleigh-geometric distribution with applications

On the Rayleigh-geometric distribution with applications

An extension of the generalized linear failure rate distribution

Compounding of distributions: a survey and new generalized classes

Contact Info

Product

Resources

About