Estimación bayesiana de la función de fiabilidad y de la tasa de riesgo de una distribución de Weibull de tiempo de fallos

Sinha, Sanjeev

doi:10.1007/bf02863554

Cited by 23 publications

(15 citation statements)

References 2 publications

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“…See Sinha [2] for more detail. Taking the scale parameter λ estimation, where ( 4 e e e 6 e e 6 e 6 e 6 e 2 e e e e i i i…”

Section: Lindley's Approximationunclassified

“…The most used methods, which are considered to be the traditional methods, are maximum likelihood and the moment estimation (Cohen and Whitten, [1]). Sinha [2] estimated the parameters of Weibull distribution by maximum likelihood and Bayesian using Lindley's approximation. Smith [3] developed the maximum likelihood and Bayesian estimators and compared them using the three-parameter Weibull distribution.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the Bayesian Methods on Interval-Censored Data for Weibull Distribution

Ahmed¹

2014

OJS

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This study considers the estimation of Maximum Likelihood Estimator and the Bayesian Estimator of the Weibull distribution with interval-censored data. The Bayesian estimation can't be used to solve the parameters analytically and therefore Markov Chain Monte Carlo is used, where the full conditional distribution for the scale and shape parameters are obtained via Metropolis-Hastings algorithm. Also Lindley's approximation is used. The two methods are compared to maximum likelihood counterparts and the comparisons are made with respect to the mean square error (MSE) to determine the best for estimating of the scale and shape parameters.

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“…See Sinha [2] for more detail. Taking the scale parameter λ estimation, where ( 4 e e e 6 e e 6 e 6 e 6 e 2 e e e e i i i…”

Section: Lindley's Approximationunclassified

Section: Introductionmentioning

confidence: 99%

Comparison of the Bayesian Methods on Interval-Censored Data for Weibull Distribution

Ahmed¹

2014

OJS

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“…Jeffreys suggested that π(θ) ∝ det(I(θ)) 1/2 be considered as a prior for the likelihood function L(θ). The Jeffreys prior is justified on the grounds of its invariance under parametrization 16 . With one parameter, the Jeffreys vague prior is…”

Section: Non-informative Priormentioning

confidence: 99%

Generalized Bayesian non-informative prior estimation of Weibull parameter with interval censoring

Guure¹,

Ibrahim²

2013

ScienceAsia

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Interval-censored data consist of adjacent inspection times that surround an unknown failure time. We seek to determine the best estimator for the Weibull scale parameter using interval-censored survival data. Consideration is given to the classical maximum likelihood and Bayesian estimation under squared error loss with interval censoring using noninformative prior and a proposed generalization of non-informative prior. The study is based on simulation and comparisons are made using mean squared error and absolute bias. We find that the proposed generalized non-informative prior is the preferred estimator of the scale parameter.

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“…Some researchers have made comparisons of MLE and that of the Bayesian approach in estimating the survival function and the parameters of the Weibull distribution. According to [20] determined the Bayes estimates of the reliability function and the hazard rate of the Weibull failure time distribution by employing squared error loss function, [1] studied the approximate Bayesian estimates for the Weibull reliability function and hazard rate from censored data by employing a new method that has the potential of reducing the number of terms in Lindley procedure, and [5] conducted a study on Bayesian survival estimator for Weibull distribution with censored data using squared error loss function with Jeffreys prior amongst others. [10] applied Bayesian estimation, for the two-parameter Weibull distribution using extension of Jeffreys" prior information with three loss functions, [21] considered Bayesian estimation and prediction for Weibull model with progressive censoring.…”

Section: …………………………………………………………………………………………………… Introduction:-mentioning

confidence: 99%

Estimation of the Survival Function Under the Constant Shape Bi-Weibull Failure Time Distribution Based on Three Loss Functions.

Lavanya¹,

Alexander²

2016

IJAR

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As a result of the adaptability in fitting time-to-failure of a very widespread multiplicity to multifaceted mechanisms, the Weibull distribution has assumed the centre stage especially in the field of life-testing and reliability/survival analysis. It has shown to be very useful for modeling and analyzing life time data in medical, biological and engineering sciences, Lawless [17]. Much of the attractiveness of the Weibull distribution is due to the wide variety of shapes it can assume by altering its parameters. According to [19], "A data sample is said to be censored when, either by accident or design, the value of the variables under investigation is unobserved for some of the items in the sample." Maximum Likelihood Estimator (MLE) is quiet efficient and very popular both in literature and practice. Bayesian approach has been employed for estimating parameters. Some researchers have made comparisons of MLE and that of the Bayesian approach in estimating the survival function and the parameters of the Weibull distribution. According to [20] determined the Bayes estimates of the reliability function and the hazard rate of the Weibull failure time distribution by employing squared error loss function, [1] studied the approximate Bayesian estimates for the Weibull reliability function and hazard rate from censored data by employing a new method that has the potential of reducing the number of terms in Lindley procedure, and [5] conducted a study on Bayesian survival estimator for Weibull distribution with censored data using squared error loss function with Jeffreys prior amongst others.[10] applied Bayesian estimation, for the two-parameter Weibull distribution using extension of Jeffreys" prior information with three loss functions, [21] considered Bayesian estimation and prediction for Weibull model with progressive censoring. Other recent papers employing different

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Estimación bayesiana de la función de fiabilidad y de la tasa de riesgo de una distribución de Weibull de tiempo de fallos

Cited by 23 publications

References 2 publications

Comparison of the Bayesian Methods on Interval-Censored Data for Weibull Distribution

Comparison of the Bayesian Methods on Interval-Censored Data for Weibull Distribution

Generalized Bayesian non-informative prior estimation of Weibull parameter with interval censoring

Estimation of the Survival Function Under the Constant Shape Bi-Weibull Failure Time Distribution Based on Three Loss Functions.

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