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Effect of autocorrelation estimators on the performance of the X control chart
16 references·8 citing·31 second hop
Effect of autocorrelation estimators on the performance of the X control chart (2018)Effect of autocorrelation estimators on t…Guaranteed Conditional Performance of Control Charts via Bootstrap Methods (2016)Guaranteed Conditional Perfor…The Difficulty in Designing Shewhart X and X Control Charts with Estimated Parameters (2015)The Difficulty in Designing S…Guaranteed conditional performance of theS2control chart with estimated parameters (2015)Guaranteed conditional perfor…Another Look at the EWMA Control Chart with Estimated Parameters (2015)Another Look at the EWMA Cont…A Reevaluation of the Adaptive Exponentially Weighted Moving Average Control Chart When Parameters are Estimated (2014)A Reevaluation of the Adaptiv…Some Recent Developments on the Effects of Parameter Estimation on Control Charts (2013)Some Recent Developments on t…Guaranteed Conditional Performance of Control Charts via Bootstrap Methods (2013)Guaranteed Conditional Perfor…Exponential CUSUM Charts with Estimated Control Limits (2013)Exponential CUSUM Charts with…Distribution-free exponentially weighted moving average control charts for monitoring unknown location (2012)Distribution-free exponential…Assessing the effect of estimation error on risk-adjusted CUSUM chart performance (2011)Assessing the effect of estim…Effect of measurement error and autocorrelation on theX¯chart (2010)Effect of measurement error a…A CUSUM control chart for monitoring the variance when parameters are estimated (2010)A CUSUM control chart for mon…Phase I Statistical Process Control Charts: An Overview and Some Results (2008)Phase I Statistical Process C…Effects of Parameter Estimation on Control Chart Properties: A Literature Review (2006)Effects of Parameter Estimati…The Performance of Exponentially Weighted Moving Average Charts With Estimated Parameters (2001)The Performance of Exponentia…Time Series Analysis Forecasting and Control. (1971)Time Series Analysis Forecast…Phase I control chart for individual autocorrelated data: application to prescription opioid monitoring (2023)Phase I control chart for ind…A new double sampling scheme to monitor the process mean of autocorrelated observations using an AR(1) model with a skip sampling strategy (2021)A new double sampling scheme …On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme (2020)On monitoring the process mea…On autoregressive model selection for the exponentially weighted moving average control chart of residuals in monitoring the mean of autocorrelated processes (2020)On autoregressive model selec…A control chart to monitor the process mean based on inspecting attributes using control limits of the traditional X-bar chart (2020)A control chart to monitor th…A combined mixed- s -skip sampling strategy to reduce the effect of autocorrelation on the X scheme with and without measurement errors (2020)A combined mixed- s -skip sam…Side-sensitive synthetic and runs-rules charts for monitoring AR(1) processes with skipping sampling strategies (2019)Side-sensitive synthetic and …Shewhart-type monitoring schemes with supplementary w-of-w runs-rules to monitor the mean of autocorrelated samples (2019)Shewhart-type monitoring sche…A review of multivariate control charts (1995)Cumulative Sum Charts and Charting for Quality Improvement (1998)Introduction to Statistical Quality Control (2005)Current research on profile monitoring (2007)An Overview of Phase I Analysis for Process Improvement and Monitoring (2014)Some Current Directions in the Theory and Application of Statistical Process Monitoring (2014)The Shewhart Control Chart—Tests for Special Causes (1984)Multivariate Generalizations of Cumulative Sum Quality-Control Schemes (1988)A Multivariate Exponentially Weighted Moving Average Control Chart (1992)The EWMA median chart with estimated parameters (2015)Control Chart Tests Based on Geometric Moving Averages (1959)Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements (1990)Introduction to Statistical Quality Control (1992)Statistical Methods for Quality Improvement (2000)A New Distribution‐free Control Chart for Joint Monitoring of Unknown Location and Scale Parameters of Continuous Distributions (2013)Control Charts for Joint Monitoring of Mean and Variance: An Overview (2013)Control Chart Tests Based on Geometric Moving Averages (2000)A control chart based on a change-point model for monitoring linear profiles (2006)Multivariate Statistical Process Control Using LASSO (2009)A Multivariate Sign EWMA Control Chart (2011)An Approach to the Probability Distribution of Cusum Run Length (1972)Control chart applications in healthcare: a literature review (2018)CONTINUOUS INSPECTION SCHEMES (1954)Optimal Stopping Times for Detecting Changes in Distributions (1986)Rethinking control chart design and evaluation (2019)Economic Control of Quality of Manufactured Product. (1932)International Journal of Production Research (1962)An overview and perspective on social network monitoring (2016)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Some recent development in a concept of causality (1988)Unit root tests in panel data: asymptotic and finite-sample properties (2002)
this work references cited by second hop

References

  • 1971Time Series Analysis Forecasting and Control.6,714 cited
  • 2006Effects of Parameter Estimation on Control Chart Properties: A Literature Review582 cited
  • 2001The Performance of Exponentially Weighted Moving Average Charts With Estimated Parameters277 cited
  • 2008Phase I Statistical Process Control Charts: An Overview and Some Results180 cited
  • 2013Some Recent Developments on the Effects of Parameter Estimation on Control Charts178 cited
  • 2015The Difficulty in Designing Shewhart X and X Control Charts with Estimated Parameters157 cited
  • 2015Another Look at the EWMA Control Chart with Estimated Parameters156 cited
  • 2013Guaranteed Conditional Performance of Control Charts via Bootstrap Methods147 cited
  • 2016Guaranteed Conditional Performance of Control Charts via Bootstrap Methods147 cited
  • 2011Assessing the effect of estimation error on risk-adjusted CUSUM chart performance118 cited
  • 2013Exponential CUSUM Charts with Estimated Control Limits116 cited
  • 2010Effect of measurement error and autocorrelation on theX¯chart102 cited
  • 2012Distribution-free exponentially weighted moving average control charts for monitoring unknown location100 cited
  • 2010A CUSUM control chart for monitoring the variance when parameters are estimated97 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2014A Reevaluation of the Adaptive Exponentially Weighted Moving Average Control Chart When Parameters are Estimated81 cited
  • 2015Guaranteed conditional performance of theS2control chart with estimated parameters79 cited
  • Cited by

    • 2020On autoregressive model selection for the exponentially weighted moving average control chart of residuals in monitoring the mean of autocorrelated processes19 cited
    • 2020A combined mixed- s -skip sampling strategy to reduce the effect of autocorrelation on the X scheme with and without measurement errors19 cited
    • 2019Side-sensitive synthetic and runs-rules charts for monitoring AR(1) processes with skipping sampling strategies18 cited
    • 2019Shewhart-type monitoring schemes with supplementary w-of-w runs-rules to monitor the mean of autocorrelated samples18 cited
    • 2023Phase I control chart for individual autocorrelated data: application to prescription opioid monitoring17 cited
    • 2021A new double sampling scheme to monitor the process mean of autocorrelated observations using an AR(1) model with a skip sampling strategy16 cited
    • 2020A control chart to monitor the process mean based on inspecting attributes using control limits of the traditional X-bar chart15 cited
    • 2020On monitoring the process mean of autocorrelated observations with measurement errors using the w‐of‐w runs‐rules scheme14 cited