The Poisson-exponential lifetime distribution

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

doi:10.1016/j.csda.2010.05.033

Cited by 116 publications

(96 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, for comparison of a nonnested survival model, under certain conditions of regularity, the distribution of the statistical likelihood ratio under H 0 is a mixture with a weights (0.5 and 0.5) of distribution χ 2 with a degree of freedom with a discrete distribution with the mass concentrated in the value 0, this is, P(ω n ≤ w) = 1/2 + 1/2P(χ 2 1 ≤ w). More details are found in Maller and Zhou (1995) and Cancho et al (2011).…”

Section: Model Selectionmentioning

confidence: 99%

Application of the Weibull-Poisson long-term survival model

Vigas

Mazucheli

Louzada

2017

CSAM

View full text Add to dashboard Cite

In this paper, we proposed a new long-term lifetime distribution with four parameters inserted in a risk competitive scenario with decreasing, increasing and unimodal hazard rate functions, namely the Weibull-Poisson long-term distribution. This new distribution arises from a scenario of competitive latent risk, in which the lifetime associated to the particular risk is not observable, and where only the minimum lifetime value among all risks is noticed in a long-term context. However, it can also be used in any other situation as long as it fits the data well. The Weibull-Poisson long-term distribution is presented as a particular case for the new exponentialPoisson long-term distribution and Weibull long-term distribution. The properties of the proposed distribution were discussed, including its probability density, survival and hazard functions and explicit algebraic formulas for its order statistics. Assuming censored data, we considered the maximum likelihood approach for parameter estimation. For different parameter settings, sample sizes, and censoring percentages various simulation studies were performed to study the mean square error of the maximum likelihood estimative, and compare the performance of the model proposed with the particular cases. The selection criteria Akaike information criterion, Bayesian information criterion, and likelihood ratio test were used for the model selection. The relevance of the approach was illustrated on two real datasets of where the new model was compared with its particular cases observing its potential and competitiveness.

show abstract

Section: Model Selectionmentioning

confidence: 99%

Application of the Weibull-Poisson long-term survival model

Vigas

Mazucheli

Louzada

2017

CSAM

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

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

View full text Add to dashboard Cite

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.

show abstract

The Poisson-exponential lifetime distribution

Cited by 116 publications

References 9 publications

Application of the Weibull-Poisson long-term survival model

Application of the Weibull-Poisson long-term survival model

Exponentiated Extended Weibull-Power Series Class of Distributions

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

Contact Info

Product

Resources

About