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Inference on Cpk for autocorrelated data in the presence of random measurement errors

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  • Michele Scagliarini

Abstract

The present paper examines the properties of the Cpk estimator when observations are autocorrelated and affected by measurement errors. The underlying reason for this choice of subject matter is that in industrial applications, process data are often autocorrelated, especially when sampling frequency is not particularly low, and even with the most advanced measuring instruments, gauge imprecision needs to be taken into consideration. In the case of a first-order stationary autoregressive process, we compare the statistical properties of the estimator in the error case with those of the estimator in the error-free case. Results indicate that the presence of gauge measurement errors leads the estimator to behave differently depending on the entity of error variability.

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  • Michele Scagliarini, 2010. "Inference on Cpk for autocorrelated data in the presence of random measurement errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(1), pages 147-158.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:1:p:147-158
    DOI: 10.1080/02664760902914482
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    References listed on IDEAS

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    1. W. L. Pearn & M. H. Shu & B. M. Hsu, 2005. "Testing process capability based on Cpm in the presence of random measurement errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(10), pages 1003-1024.
    2. Nien Fan Zhang, 1998. "Estimating process capability indexes for autocorrelated data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(4), pages 559-574.
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    Cited by:

    1. Michele Scagliarini, 2011. "Multivariate process capability using principal component analysis in the presence of measurement errors," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 113-128, June.

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