Process capability analysis in the presence of autocorrelation

Noorossana, Rassoul

doi:10.1002/qre.445

Cited by 31 publications

(16 citation statements)

References 14 publications

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“…where c, f and b are defined as in (10), (11) and (12), respectively. Taking into account the fact that…”

Section: The Suggested Methodsmentioning

confidence: 99%

“…In most of the cases it is assumed that the studied process is normally distributed and the observations produced through it are independent. Statistical inference on capability indices if any of these two basic assumptions is violated becomes a more difficult task and has attracted a small number of authors, such as Zhang 10 and Noorossana 11 , who studied the case where the collected data are autocorrelated, and Clements 12 , Pearn and Chen 13 , Chen and Pearn 14 , Tang and Than 15 , Wu and Swain 16 and Chang et al 17 , who considered the case of non-normality.…”

Section: Introductionmentioning

confidence: 98%

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A New Method for Constructing Confidence Intervals for the Index C_pm

Perakis

Xekalaki

2004

Quality & Reliability Eng

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In the statistical literature on the study of the capability of processes through the use of indices, C pm appears to have been one of the most widely used capability indices and its estimation has attracted much interest. In this article, a new method for constructing approximate confidence intervals or lower confidence limits for this index is suggested. The method is based on an approximation of the non-central chisquare distribution, which was proposed by Pearson. Its coverage appears to be more satisfactory compared with that achieved by any of the two most widely used methods that were proposed by Boyles, in situations where one is interested in assessing a lower confidence limit for C pm . This is supported by the results of an extensive simulation study.

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“…where c, f and b are defined as in (10), (11) and (12), respectively. Taking into account the fact that…”

Section: The Suggested Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

A New Method for Constructing Confidence Intervals for the Index C_pm

Perakis

Xekalaki

2004

Quality & Reliability Eng

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show abstract

“…In a recent research work Vännmam and Kulahci [15] presented a modelfree method, based on the "iterative skipping strategy", of performing capability analysis when data are autocorrelated. Some further studies dealing with capability indices and autocorrelated data can be found in Noorossana [12] and Chen et al [6].…”

Section: Scagliarinimentioning

confidence: 98%

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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“…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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Process capability analysis in the presence of autocorrelation

Cited by 31 publications

References 14 publications

A New Method for Constructing Confidence Intervals for the Index C_pm

A New Method for Constructing Confidence Intervals for the Index C_pm

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

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

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Process capability analysis in the presence of autocorrelation

Cited by 31 publications

References 14 publications

A New Method for Constructing Confidence Intervals for the Index Cpm

A New Method for Constructing Confidence Intervals for the Index Cpm

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

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

Contact Info

Product

Resources

About

A New Method for Constructing Confidence Intervals for the Index C_pm

A New Method for Constructing Confidence Intervals for the Index C_pm

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