DEST - Departamento de Estatística

URI Permanente desta comunidadehttps://repositorio.ufrn.br/handle/1/136

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Prediction of failure probability of oil wells
(Brazilian Statistical Association, 2014) Carvalho, João B.; Valença, Dione M.; Singer, Julio M.
We consider parametric accelerated failure time models with random effects to predict the probability of possibly correlated failures occurring in oil wells. In this context, we first consider empirical Bayes predictors (EBP) based on aWeibull distribution for the failure times and on a Gaussian distribution for the random effects.We also obtain empirical best linear unbiased predictors (EBLUP) using a linear mixed model for which the form of the distribution of the random effects is not specified. We compare both approaches using data obtained from an oil-drilling company and suggest how the results may be employed in designing a preventive maintenance program.
Testing inference in accelerated failure time models
(Canadian Center of Science and Education, 2014-04) Medeiros, Francisco M. C.; Silva-Júnior, Antônio H. M. da; Valença, Dione M.; Ferrari, Silvia L. P.
We address the issue of performing hypothesis testing in accelerated failure time models for non-censored and censored samples. The performances of the likelihood ratio test and a recently proposed test, the gradient test, are compared through simulation. The gradient test features the same asymptotic properties as the classical large sample tests, namely, the likelihood ratio, Wald and score tests. Additionally, it is as simple to compute as the likelihood ratio test. Unlike the score and Wald tests, the gradient test does require the computation of the information matrix, neither observed nor expected. Our study suggests that the

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Navegando DEST - Departamento de Estatística por Assunto "Accelerated failure time models"