1987
DOI: 10.2307/2347795
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A Comparison of Maximum Likelihood and Bayesian Estimators for the Three- Parameter Weibull Distribution

Abstract: Maximum likelihood and Bayesian estimators are developed and compared for the three-parameter Weibull distribution. For the data analysed in the paper, the two sets of estimators are found to be very different. The reasons for this are explored, and ways of reducing the discrepancy, including reparametrization, are investigated. Our overall conclusion is that there are practical advantages to the Bayesian approach.

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Cited by 427 publications
(276 citation statements)
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“…The Maximum Likelihood Estimation (MLE) method is a procedure to estimate the parameters of statistical models [10]. The method highlights the smallest coefficient of variation and uses the likelihood of a particular distribution's density function to determine the model that most represents experimental results [11].…”
Section: Best-fit Proceduresmentioning
confidence: 99%
“…The Maximum Likelihood Estimation (MLE) method is a procedure to estimate the parameters of statistical models [10]. The method highlights the smallest coefficient of variation and uses the likelihood of a particular distribution's density function to determine the model that most represents experimental results [11].…”
Section: Best-fit Proceduresmentioning
confidence: 99%
“…The likelihoods for these models are not much more complicated than those for the two-parameter modeIs, although care has to be taken with the estimation of location parameters (Newby 1988;Smith & Naylor 1987). Generally a grouped likelihood performs better than the Iikelihood itself (Cheng & Amin 1983;Cheng & lies 1987).…”
Section: Acceleratcd Failure Timementioning
confidence: 99%
“…Unfortunately, the units of measurements are not given in the paper, and they are taken from Smith and Naylor (1987) 0 It is obvious from the fitting of QLD, Lindley and exponential distributions in the table 1 that QLD gives much closer fit than Lindley and exponential distributions in all data-sets, and therefore QLD can be preferred over Lindley and exponential distributions for modeling lifetime data-sets from biomedical science, engineering and other fields of knowledge.…”
Section: Goodness Of Fit To Real Lifetime Datasetsmentioning
confidence: 99%