Inference on C p Index For Auto-correlated Data Contaminated With Random Measurement Errors

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Conference Article

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Proceedings of the 24th ISSAT International Conference on Reliability and Quality in Design


The present work examines the properties of the process capability index C p when the observations are autocorrelated and also affected by measurement errors. The underlying reason for this choice of subject matter is that in many industrial applications (e.g. in chemical and pharmaceutical industries), process data are often autocorrelated. Moreover, with the development of measurement technology and data acquisition technology, sampling frequency is getting higher and the existence of autocorrelation cannot be ignored. Furthermore, 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.

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