ESTIMATION OFCpFOR AUTOCORRELATED DATA AND MEASUREMENT ERRORS

Scagliarini, Michele

doi:10.1081/sta-120013018

Cited by 32 publications

(20 citation statements)

References 8 publications

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“…Pearn et al [13] conducted a sensitivity analysis of C p and C pm in the presence of gauge measurement errors, and Scagliarini [14] examined the joint condition of "autocorrelation and measurement errors" by analysing the properties of the C p estimator for autocorrelated data in the presence of measurement errors.…”

Section: Scagliarinimentioning

confidence: 99%

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

Scagliarini

2009

Journal of Applied Statistics

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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.

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Section: Scagliarinimentioning

confidence: 99%

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

Scagliarini

2009

Journal of Applied Statistics

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“…However, according to Kotz & Johnson (1993), approximate expressions can be derived by statistical differential method (Scagliarini 2002),…”

Section: Estimating C P With Autocorrelated Datamentioning

confidence: 99%

Process capability analysis and estimation scheme for autocorrelated data

Sun

Wang

Zhang

2010

J. Syst. Sci. Syst. Eng.

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Autocorrelation is prevalent in continuous production processes, such as the processes in the chemical and pharmaceutical industries. With the development of measurement technology and data acquisition technology, sampling frequency is getting higher and the existence of autocorrelation cannot be ignored. This paper analyzes five estimation schemes of process capability for autocorrelated data. Comparisons among these schemes are discussed for small sample and large sample. In conclusion, this paper gives a procedure of process capability analysis for autocorrelated data.

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“…He shows, through simulations, that if autocorrelation is ignored when calculating confidence intervals, the empirical coverage rate differs considerably from the nominal one. Some further studies dealing with capability indices and autocorrelated data can be found in Noorossana 18 , Scagliarini 19 , and Chen et al 20 .…”

Section: Introductionmentioning

confidence: 97%

A model‐free approach to eliminate autocorrelation when testing for process capability

Vännman

Külahçı

2007

Quality & Reliability Eng

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There is an increasing use of on-line data acquisition systems in industry. This usually leads to autocorrelated data and implies that the assumption of independent observations has to be re-examined. Most decision procedures for capability analysis assume independent data. In this article we present a new way of performing capability analysis when data are autocorrelated. This method is based on what can be called the 'iterative skipping' strategy. In that, by skipping a pre-determined number of observations, e.g. considering every fifth observation, the data set is divided into subsamples for which the independence assumption may be valid. For each such subsample of the data we estimate a capability index. Then traditional tests, assuming independence, can be performed based on each estimated capability index from the subsamples. By combining the information from each test statistic based on the subsamples in a suitable way, a new and efficient decision procedure is obtained. We discuss different ways of combining the information from these individual tests. A main appeal of our proposed method is that no time-series model is needed.

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ESTIMATION OFC_pFOR AUTOCORRELATED DATA AND MEASUREMENT ERRORS

Cited by 32 publications

References 8 publications

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

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

Process capability analysis and estimation scheme for autocorrelated data

A model‐free approach to eliminate autocorrelation when testing for process capability

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ESTIMATION OFCpFOR AUTOCORRELATED DATA AND MEASUREMENT ERRORS

Cited by 32 publications

References 8 publications

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

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

Process capability analysis and estimation scheme for autocorrelated data

A model‐free approach to eliminate autocorrelation when testing for process capability

Contact Info

Product

Resources

About

ESTIMATION OFC_pFOR AUTOCORRELATED DATA AND MEASUREMENT ERRORS

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

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