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Multivariate process capability a bayesian perspective

 

作者: Murali Niverthi,   Dipak K. Dey,  

 

期刊: Communications in Statistics - Simulation and Computation  （Taylor Available online 2000）
卷期: Volume 29, issue 2  

页码: 667-687

 

ISSN:0361-0918

 

年代: 2000

 

DOI:10.1080/03610910008813634

 

出版商: Marcel Dekker, Inc.

 

关键词: Bayesian p-values;complete conditional distribution;discrepancy measures;Gibbs sampler;Markov chain Monte Carlo;model diagnostics;multivariate process capability index

 

数据来源: Taylor

 

摘要:

In this paper an attempt has been made to examine the multivariate versions of the common process capability indices (PCI's) denoted byCpandCpk. Markov chain Monte Carlo (MCMC) methods are used to generate sampling distributions for the various PCI's from where inference is performed. Some Bayesian model checking techniques are developed and implemented to examine how well our model fits the data. Finally the methods are exemplified on a historical aircraft data set collected by the Pratt and Whitney Company.

 

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