Comparing confidence limits for short-run process incapability index Cpp

Jau-Chuan Ke, Yunn-Kuang Chu and Jer-Yan Lin

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 13, 3227-3238

Abstract: Process incapability index Cpp has been proposed in the manufacturing industry to assess process incapability. In industries it is sometimes unable to get large samples, and, hence, the CAN (consistent and asymptotically normal) property of the unbiased estimator for Cpp is missing. In this paper, six bootstrap methods are applied to construct upper confidence bounds (UCBs) of Cpp for short-urn production processes where sample size is small; standard bootstrap (SB), Bayesian bootstrap (BB), bootstrap pivotal (BP), percentile bootstrap (PB), bias-corrected percentile bootstrap (BCPB), and bias-corrected and accelerated bootstrap (BCa). A numerical simulation study is conducted in order to demonstrate the performance of the six various estimation methods. We further investigate the accuracy of the six methods by calculating the relative coverage (defined as the ratio of coverage percentage to average length of UCB). Detailed discussions of simulation results for seven short-run processes are presented. Finally, one real example from Ford Company’s Windsor Casting Plant is used to illustrate the six interval estimation methods.

Keywords: Bootstrap resampling; Coverage percentage; Bayesian bootstrap; Bootstrap pivotal; Upper confidence limit; Process incapability index; Relative coverage (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:13:p:3227-3238

DOI: 10.1016/j.physa.2008.01.100

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