Empirical Bayes decision rule for classification on defective items in Weibull distribution

Chang, Shui-Ching; Li, Tze Fen

doi:10.1016/j.amc.2006.04.001

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…Recently, Chang and Li [3] assumed that the lifetime of an item has a Weibull distribution and proposed an empirical Bayes (EB) classifier to clarity defective items. They conclude that the EB classifier can adapt itself to a better and more accurate decision rule by making use of new items, when this classifier is put in use.…”

Section: Discussionmentioning

confidence: 99%

Comparison between method of moments and entropy regularization algorithm applied to parameter estimation for mixed-Weibull distribution

Hung

Chang

2011

Journal of Applied Statistics

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Mixed-Weibull distribution has been used to model a wide range of failure data sets, and in many practical situations the number of components in a mixture model is unknown. Thus, the parameter estimation of a mixed-Weibull distribution is considered and the important issue of how to determine the number of components is discussed. Two approaches are proposed to solve this problem. One is the method of moments and the other is a regularization type of fuzzy clustering algorithm. Finally, numerical examples and two real data sets are given to illustrate the features of the proposed approaches.

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Section: Discussionmentioning

confidence: 99%

Comparison between method of moments and entropy regularization algorithm applied to parameter estimation for mixed-Weibull distribution

Hung

Chang

2011

Journal of Applied Statistics

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“…Robbins [11] introduced the empirical Bayes (EB) methods and applied in the problems where the researchers wish to make inferential statements about the unknown hyper-parameters. Some works on empirical Bayes estimation procedure have been worked out in [26], [19], [3], [13], [6], [30] etc. Hierarchical Bayesian method is more robust than ordinary Bayes method as it involves multiple stages for estimating the parameters involved in the prior distribution.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study between Partial Bayes and Empirical Bayes Method in Gamma Distribution

Seal,

Bhunia,

Banerjee

2023

Stat., optim. inf. comput.

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Though the name Partial Bayes was used earlier in a different context, but in statistics this is started from 2021, (Banerjee and Seal, 2021). Also, we know that empirical Bayes method was studied extensively for several decades. In this paper, these two methods are compared in two parameter gamma distribution having shape and scale parameter. As expected, it is found that empirical Bayes method is good in some cases. However, partial Bayes method performs even better in some cases where the shape parameter is sufficiently small, i.e. variation in the data is small. Even, overall performances of these two methods do not differ too much. But whenever we have information that shape parameter is small, then in that case partial Bayes method performs well. These results are also found by extensive simulation technique. The performances of these two estimators are also compared using two real datasets.

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Patent value analysis using support vector machines

Ercan

Kayakutlu

2013

Soft Comput

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Empirical Bayes decision rule for classification on defective items in Weibull distribution

Cited by 7 publications

References 25 publications

Comparison between method of moments and entropy regularization algorithm applied to parameter estimation for mixed-Weibull distribution

Comparison between method of moments and entropy regularization algorithm applied to parameter estimation for mixed-Weibull distribution

Comparative Study between Partial Bayes and Empirical Bayes Method in Gamma Distribution

Patent value analysis using support vector machines

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