The long-term exponentiated complementary exponential geometric distribution under a latent complementary causes framework

Louzada, Francisco; Yamachi, Cintia Y.; Marchi, Vitor A. A.; Franco, Maria Aparecida Paiva

doi:10.5540/tema.2014.015.01.0019

Cited by 2 publications

(1 citation statement)

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“…Order statistics play an important role in quality control testing and reliability, where a practitioner needs to predict the failure of future items based on how many times develop early failure. These predictors are often based on moments of order statistics (Louzada et al, 2014). Considering LWP distribution, the pdf of k th order statistic is given as follows.…”

Section: Characteristics Of the Long-term Weibull-poisson Distributionmentioning

confidence: 99%

Application of the Weibull-Poisson long-term survival model

Vigas

Mazucheli

Louzada

2017

CSAM

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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.

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Section: Characteristics Of the Long-term Weibull-poisson Distributionmentioning

confidence: 99%