A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems

Prataviera, Fábio; Ortega, Edwin M. M.; Cordeiro, Gauss M.; Pescim, Rodrigo R.; Verssani, Bruna A. W.

doi:10.1016/j.ress.2018.03.034

Cited by 39 publications

(23 citation statements)

References 38 publications

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“…Vigas et al [26] used the Poisson-Weibull distribution to explain the survival time of the patients who were admitted into a heart transplant program using the year of acceptance to the program, the age of the patient, information about previous surgery (where one was yes and zero was no) and if the patient had the transplant within the program (where one was yes and zero was no) as covariates. Prataviera et al [27] considered a generalized odd log-logistic flexible Weibull regression model to explain the breaking time for a part located in a sugarcane cutting system using the agent causing the failure as the covariate.…”

Section: Weibull Regression Mixed Modelmentioning

confidence: 99%

The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Hernández

Giampaoli

2018

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Mixed models are useful tools for analyzing clustered and longitudinal data. These models assume that random effects are normally distributed. However, this may be unrealistic or restrictive when representing information of the data. Several papers have been published to quantify the impacts of misspecification of the shape of the random effects in mixed models. Notably, these studies primarily concentrated their efforts on models with response variables that have normal, logistic and Poisson distributions, and the results were not conclusive. As such, we investigated the misspecification of the shape of the random effects in a Weibull regression mixed model with random intercepts in the two parameters of the Weibull distribution. Through an extensive simulation study considering six random effect distributions and assuming normality for the random effects in the estimation procedure, we found an impact of misspecification on the estimations of the fixed effects associated with the second parameter σ of the Weibull distribution. Additionally, the variance components of the model were also affected by the misspecification.

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Section: Weibull Regression Mixed Modelmentioning

confidence: 99%

The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

Hernández

Giampaoli

2018

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“…Furthermore, the MCMC error decreases with the increase of sample size. 6 10.478 10 -0.405 3 1.445 5 -0.394 2 -0.191 1 1.555 9 1.042 4 1.522 7 RM SE(^ ) 1.552 8 1.541 7 11.406 10 0.405 2 1.457 5 0.400 1 0.898 3 1.582 9 1.056 4 1.537 6 Bias(^ ) -0.373 4 -0.374 5 0.000 1 57.713 10 -0.378 6 4.881 8 11.130 9 -0.373 2 -0.382 7 -0.373 3 RM SE(^ ) 0.374 3 0.375 5 0.000 1 60.414 10 0.378 6…”

Section: Simulation Studymentioning

confidence: 99%

“…[5] proposed a new extension of FW distribution using the odd generalized exponential generator. [6] proposed a generalized class of FW distribution for repairable systems. [7] proposed a generalized class of FW distribution.…”

Section: Introductionmentioning

confidence: 99%

A comparison of different methods of estimation for the Flexible Weibull distribution

Ali

Dey²,

Tahir³

et al. 2020

Communications Faculty of Science University of Ankara Series A1Mathematics and Statistics

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This article presents di¤erent parameter estimation methods for ‡exible Weibull distribution introduced by Bebbington et al. (Reliability Engineering and System Safety 92:719-726, 2007), which is a modi…ed version of the Weibull distribution and is suitable to model di¤erent shapes of the hazard rate. We consider both frequentist and Bayesian estimation methods and present a comprehensive comparison of them. For frequentist estimation, we consider the maximum likelihood estimators, least squares estimators, weighted least squares estimators, percentile estimators, the maximum product spacing estimators, the minimum spacing absolute distance estimators, the minimum spacing absolute log-distance estimators, Cramér von Mises estimators, Anderson Darling estimators, and right tailed Anderson Darling estimators, and compare them using a comprehensive simulation study. We also consider Bayesian estimation by assuming gamma priors for both shape and scale parameters. We use a Markov Chain Monte Carlo algorithm to compute the posterior summaries. A real data example is also a part of this work.

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“…r(t) corresponding to Figure 2a,b are increasing-decreasing-increasing. This kind of hazard functions has been recently introduced and discussed in literature, due to its interest in reliability of systems, see for instance [23] or [24] (and references therein). In plot for Figure 2b, r(t) is (quickly) increasing-or (quickly) decreasing.…”

Section: Remarkmentioning

confidence: 99%

Flexible Birnbaum–Saunders Distribution

et al. 2019

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In this paper, we propose a bimodal extension of the Birnbaum-Saunders model by including an extra parameter. This new model is termed flexible Birnbaum-Saunders (FBS) and includes the ordinary Birnbaum-Saunders (BS) and the skew Birnbaum-Saunders (SBS) model as special cases. Its properties are studied. Parameter estimation is considered via an iterative maximum likelihood approach. Two real applications, of interest in environmental sciences, are included, which reveal that our proposal can perform better than other competing models.

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A new generalized odd log-logistic flexible Weibull regression model with applications in repairable systems

Cited by 39 publications

References 38 publications

The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

The Impact of Misspecified Random Effect Distribution in a Weibull Regression Mixed Model

A comparison of different methods of estimation for the Flexible Weibull distribution

Flexible Birnbaum–Saunders Distribution

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