Multivariate process capability a bayesian perspective

Abstract

In this paper an attempt has been made to examine the multivariate versions of the common process capability indices (PCI's) denoted by C_p and C_pk . 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.

Keywords:

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