Browsing by Author "Feltz, CJ"

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An empirical Bayes process monitoring technique for polytomous data
(2005-02-01)
Shiau, JJH
;
Chen, CR
;
Feltz, CJ
;
統計學研究所
;
Institute of Statistics
When a product item is tested, usually one has more information than just pass or fail. Often there are categories of failure modes. The purpose of this paper is to develop a method to monitor the fractions of the tested items falling into different categories of pass/fail modes. Using the multinomial model with Dirichlet prior, we describe the theory underlying an empirical Bayes approach to monitoring polytomous data generated in manufacturing processes. A pseudo maximum likelihood estimator (PMLE) and the method-of-moments estimator (MME) of the hyperparameters of the prior distribution are considered and compared by a simulation study. It is found that the PMLE performs slightly better than the MME. A monitoring scheme based on the marginal distributions of the observed pass/fail fractions is proposed. The average run length behavior of the proposed monitoring scheme is investigated. Finally, an example to illustrate the use of the technique is given. Copyright (C) 2004 John Wiley Sons, Ltd.
Statistical process monitoring using an empirical Bayes multivariate process control chart
(2001-03-01)
Feltz, CJ
;
Shiau, JJH
;
統計學研究所
;
Institute of Statistics
In this paper, we describe the theory underlying an empirical Bayesian approach to monitoring two or more process characteristics simultaneously. If the, data is continuous and multivariate in nature, often the multivariate normal distribution can be used to model the process. Then, using Bayesian theory: we develop techniques to implement empirical Bayes process monitoring of the multivariable process. Lastly, an example is given to illustrate the use of our techniques. Copyright (C) 2001 John Wiley & Sons, Ltd.