A compound class of Weibull and power series distributions

Morais, Alice Lemos de; Barreto‐Souza, Wagner

doi:10.1016/j.csda.2010.09.030

Cited by 143 publications

(100 citation statements)

References 21 publications

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“…The Weibull-geometric (WG), Exponential-Poisson (EP), Weibull-Power-Series (WPS), Complementary-Exponential-geometric (CEG), Exponential-Geometric (EG), Generalized-Exponential-Power-Series (GEPS), Exponential Weibull-Poisson (EWP), and Generalized-Inverse-Weibull-Poisson (GIWP) distributions are introduced and presented by Adamidis and Loukas [5], Kus [6], Chahkandi and Ganjali [7], Tahmasbi and Rezaei [8], Barreto [9], Morais and Barreto [10], Barreto and Cribari [11], Louzada et al [12], and Cancho et al [13]. Hamedani and Ahsanullah [14] studied and discussed many properties of WG, such as moments, hazard functions, and functions of order statistics.…”

Section: Introductionmentioning

confidence: 99%

Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples

El-Sayed¹,

Riad²,

Elsafty³

et al. 2017

JCC

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The purpose of this article offers different algorithms of Weibull Geometric (WG) distribution estimation depending on the progressive Type II censoring samples plan, spatially the joint confidence intervals for the parameters. The approximate joint confidence intervals for the parameters, the approximate confidence regions and percentile bootstrap intervals of confidence are discussed, and several Markov chain Monte Carlo (MCMC) techniques are also presented. The parts of mean square error (MSEs) and credible intervals lengths, the estimators of Bayes depend on non-informative implement more effective than the maximum likelihood estimates (MLEs) and bootstrap. Comparing the models, the MSEs, average confidence interval lengths of the MLEs, and Bayes estimators for parameters are less significant for censored models.

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Section: Introductionmentioning

confidence: 99%

Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples

El-Sayed¹,

Riad²,

Elsafty³

et al. 2017

JCC

View full text Add to dashboard Cite

show abstract

“…Other families of lifetime distributions have been investigated by several authors. For example, Kus (2007), Tahmasbi and Rezaei (2008), Chahkandi and Ganjali (2009), Barreto-Souza and Cribari-Neto (2009), Silva et al (2010), Barreto-Souza et al (2011), Cancho et al (2011), Louzada-Neto et al (2011), Morais and Barreto-Souza (2011), Hemmati et al (2011), Alkarni and Orabi (2012), Nadarajah et al (2013), Bakouch et al (2014), and others.…”

Section: Introductionmentioning

confidence: 99%

The Exponential-Generalized Truncated Geometric (EGTG) Distribution: A New Lifetime Distribution

Rahmouni

Orabi

2017

IJSP

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This paper introduces a new two-parameter lifetime distribution, called the exponential-generalized truncated geometric (EGTG) distribution, by compounding the exponential with the generalized truncated geometric distributions. The new distribution involves two important known distributions, i.e., the exponential-geometric (Adamidis and Loukas, 1998) and the extended (complementary) exponential-geometric distributions (Adamidis et al., 2005; in the minimum and maximum lifetime cases, respectively. General forms of the probability distribution, the survival and the failure rate functions as well as their properties are presented for some special cases. The application study is illustrated based on two real data sets.

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“…Others that follow the same approach include the Weibull power series(WPS), extended Weibull power series (EWPS), exponentiated Weibull-logarithmic (EWL), exponentiated Weibull Poisson (EWP), exponentiated Weibull geometric (EWG) and exponentiated Weibull power series (EWPS) distributions proposed and studied by [27,37,24,23,25] and [26] respectively. Moreover, in recent years, some new generators of distributions based on the exponential distribution such as the odd-generalized exponential family of distributions (OGE) and odd-exponential-G family of distributions (OEG) were proposed and analyzed by [39] and [9] respectively.…”

Section: Introductionmentioning

confidence: 99%

Poisson-odd generalized exponential family of distributions: Theory and Applications

Muhammad¹

2016

HJMS

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In this paper, we introduce a new family of distributions called the Poisson-odd generalized exponential distribution (POGE). Various properties of the new model are derived and studied. The new distribution has the odd generalized exponential as its limiting distribution. We present and study two special cases of the POGE family of distributions, namely the Poisson odd generalized exponential-half logistic and the Poisson odd generalized exponential-uniform distributions. Estimation and inference procedure for the parameters of the new distribution are discussed by the method of maximum likelihood; we also evaluate the proposed estimation method by simulation studies. Applications to two real data sets are provided in order to demonstrate the performance of the proposed family of distributions.

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A compound class of Weibull and power series distributions

Cited by 143 publications

References 21 publications

Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples

Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples

The Exponential-Generalized Truncated Geometric (EGTG) Distribution: A New Lifetime Distribution

Poisson-odd generalized exponential family of distributions: Theory and Applications

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