Effects of Parameter Estimation on Control Chart Properties: A Literature Review

By Willis A. Jensen, L. A. Jones‐Farmer, Charles W. Champ, William H. Woodall. 2006

606 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b8e5d155cb4f01f829db35fd59837bcee7651059

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In Statistics and Probability

Citations in the corpus

Some Current Directions in the Theory and Application of Statistical Process Monitoring (2014, background): “Jensen et al. (2006) and Psarakis et al. (2013) provided reviews of the literature on the e↵ect of the estimation error on control-chart monitoring performance in Phase II.”
Control Charts Based on the Exponential Distribution: Adapting Runs Rules for the t Chart (2013, background): “For more information on the effect of parameter estimation on performance of control charts, see Jensen et al. (2006).”
Is Average Run Length to False Alarm Always an Informative Criterion? (2008, background): “Even in well-established applications such as quality control, although the assumption of known pre-change parameters seems to be reasonable as quality of products can be pre-defined, the issue of partially specified pre-change parameters has recently been recognized, see, for example, Jensen et al.”
Control Charts for Simultaneous Monitoring of Unknown Mean and Variance of Normally Distributed Processes (2013, background): “In their wellwritten review, Jensen et al. (2006) summarized the e↵ects of parameter estimation on the performance of a variety of control charts.”
Estimating the Standard Deviation in Quality-Control Applications (2010, background): “Use of the sample ranges is often recommended for estimating the standard deviation σ of a normally distributed quality characteristic when the sample size is very small; see, for example, Montgomery (2009) and Ott (1975).”
Another Look at the EWMA Control Chart with Estimated Parameters (2015, methodology): “The SDARL metric was proposed by Jones and Steiner (2012), who used it to determine the e↵ect of the amount of Phase I data on the risk-adjusted cumulative sum (CUSUM) control chart.”
A Phase I nonparametric Shewhart-type control chart based on the median (2010, background): “[8] presented an overview of the literature on the effects of parameter estimation in Phase I on the properties of Phase II parametric control charts and noted the lack of guidance regarding Phase I analysis in the literature.”
The Poisson INAR(1) CUSUM chart under overdispersion and estimation error (2011, methodology): “, taking into account the random variability introduced through parameter estimation (see, for example, Jensen et al. (2006) and Testik (2007)).”
The EWMA median chart with estimated parameters (2016, background): “A comprehensive list CONTACT Philippe Castagliola philippe.castagliola@univ-nantes.fr of papers in this area of research can be found in Jensen et al. (2006) and Psarakis et al. (2014).”
The Difficulty in Designing Shewhart X̄ and X Control Charts with Estimated Parameters (2015, background): “are referred to Jensen et al. (2006) and Psarakis et al.”
Shewhart control charts for dispersion adjusted for parameter estimation (2017, background): “For reviews of work performed up to 2006 see Jensen et al. (2006), and for more recent developments see Psarakis et al. (2014).”
Design and Analysis of Control Charts for Standard Deviation with Estimated Parameters (2011, neutral): “Jensen et al. (2006) conducted a literature survey of the e↵ects of parameter estimation on controlchart properties and identified several issues for future research.”
Control Charts for Poisson Count Data with Varying Sample Sizes (2010, methodology): “Because the sample size is constant, all three of these CUSUM methods, along with the Lucas (1985) CUSUM method, are equivalent.”
CONTROL CHART FOR MONITORING NONPARAMETRIC PROFILES WITH ARBITRARY DESIGN (2009, background): “To avoid such problems, a large and thus costly collection of IC profile samples would be necessary (see Jensen et al. 2006 for related discussion). Zou et al. (2008) provide a general guideline on how many IC profile samples are necessary to obtain good run length behavior for the NEWMA chart, acco”
Profile Monitoring with Binary Data and Random Predictors (2011, methodology): “In practical applications, the performance of the EWMA-GLM would be affected by the amount of data in the reference dataset (Jensen et al. (2006)).”