The Poisson–exponential distribution: a Bayesian approach

Louzada, Francisco; Cancho, Vicente G.; Barriga, Gladys Dorotea Cacsire

doi:10.1080/02664763.2010.491862

Cited by 27 publications

(22 citation statements)

References 12 publications

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“…The motivation is to give a parametric fit for real data sets where the underlying failure rates, arising on a latent competing risk problem base, present monotone shapes (nonconstant hazard rates). The proposed distributions are introduced as extensions of the exponential distribution, following Adamidis & Loukas (1998) and Kuş (2007), by compounding some useful lifetime and truncated discrete distributions (for review, see Barreto-Souza & Cribari-Neto (2009), Chahkandi & Ganjali (2009), Silva et al (2010), Barreto-Souza et al (2011), Cancho et al (2011), Louzada-Neto et al (2011), , Hemmati et al (2011), Nadarajah et al (2013), Bakouch et al (2014), and others). The genesis is stated on competing risk scenarios in presence of latent risks, i.e.…”

Section: Introductionmentioning

confidence: 99%

A generalization of the exponential-logarithmic distribution for reliability and life data analysis

Rahmouni

Orabi

2018

Life Cycle Reliab Saf Eng

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In this paper, we introduce a new two-parameter lifetime distribution, called the exponential-generalized truncated logarithmic (EGTL) distribution, by compounding the exponential and generalized truncated logarithmic distributions. Our procedure generalizes the exponential-logarithmic (EL) distribution modelling the reliability of systems by the use of first-order concepts, where the minimum lifetime is considered (Tahmasbi & Rezaei 2008). In our approach, we assume that a system fails if a given number k of the components fails and then, we consider the k th -smallest value of lifetime instead of the minimum lifetime. The reliability and failure rate functions as well as their properties are presented for some special cases. The estimation of the parameters is attained by the maximum likelihood, the expectation maximization algorithm, the method of moments and the Bayesian approach, with a simulation study performed to illustrate the different methods of estimation. The application study is illustrated based on two real data sets used in many applications of reliability.

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Section: Introductionmentioning

confidence: 99%

A generalization of the exponential-logarithmic distribution for reliability and life data analysis

Rahmouni

Orabi

2018

Life Cycle Reliab Saf Eng

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“…Cancho et al [15] proposed the PED as a lifetime model considering the only lifetime which can be observed is the maximum among the lifetimes of the considered causes of failure. Louzada-Neto et al [24] studied the statistical properties of the PED and discussed the Bayes estimators of its parameters under squared error loss function. Tomazella et al [29] considered a Bayesian reference analysis for the PED following the technique presented in Cancho et al [15].…”

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confidence: 99%

Properties, estimations and predictions for a Poisson-half-logistic distribution based on progressively type-II censored samples

Abdel-Hamid

2016

Applied Mathematical Modelling

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A new lifetime distribution with increasing-constant hazard rate is introduced. It arises by compounding the Poisson and half-logistic distributions and can be applied in complementary risk models and parallel systems. Properties of the new distribution are discussed.Based on progressive type-II censoring, maximum likelihood estimates of the involved parameters are obtained and their properties are studied via a simulation study. Prediction of future order statistics is investigated using one-and two-sample Bayesian prediction schemes. Prediction intervals are also constructed using the highest conditional density.The coverage probabilities of the prediction intervals are computed. Finally, two examples are presented, one of them is based on simulated data and the other one is based on real data, to illustrate the obtained results.

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“…The GEPS class of distributions contains complementary exponentiated exponential-geometric distribution introduced by Louzada et al (2013), complementary exponentialgeometric distribution introduced by , Poisson-exponential distribution introduced by Cancho et al (2011) and Louzada-Neto et al (2011), complementary exponential-power series class of distributions introduced by Flores et al (2013), generalized exponential distribution introduced by Gupta and Kundu (1999) and generalized exponentialgeometric distribution introduced by Bidram et al (2013) .…”

Section: Generalized Exponential-power Seriesmentioning

confidence: 99%

Exponentiated Extended Weibull-Power Series Class of Distributions

Tahmasebi

Jafari

2015

CeN

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In this paper, we introduce a new class of distributions by compounding the exponentiated extended Weibull family and power series family. This distribution contains several lifetime models such as the complementary extended Weibull-power series, generalized exponential-power series, generalized linear failure rate-power series, exponentiated Weibull-power series, generalized modified Weibull-power series, generalized Gompertz-power series and exponentiated extended Weibull distributions as special cases. We obtain several properties of this new class of distributions such as Shannon entropy, mean residual life, hazard rate function, quantiles and moments. The maximum likelihood estimation procedure via a EM-algorithm is presented.

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The Poisson–exponential distribution: a Bayesian approach

Cited by 27 publications

References 12 publications

A generalization of the exponential-logarithmic distribution for reliability and life data analysis

A generalization of the exponential-logarithmic distribution for reliability and life data analysis

Properties, estimations and predictions for a Poisson-half-logistic distribution based on progressively type-II censored samples

Exponentiated Extended Weibull-Power Series Class of Distributions

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