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Abstract
The present paper examines the properties of the C pk 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.
Acknowledgements
The author is grateful to the editor and two anonymous referees for their insightful comments and suggestions. The research work behind this paper was funded by a 2006 grant (project no.2006139812.003, sector: Economics and Statistics) for research projects of national interest, provided by the Italian Ministry of the University and Scientific and Technological Research.