Citations (23)
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Sven Knoth, Nesma A. Saleh, Mahmoud A. Mahmoud, William H. Woodall & Víctor G. Tercero-Gómez. (2023) A critique of a variety of “memory-based” process monitoring methods. Journal of Quality Technology 55:1, pages 18-42.
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Samrad Jafarian-Namin, Mohammad Saber Fallahnezhad, Reza Tavakkoli-Moghaddam & Ali Salmasnia. (2022) Desensitized control charts with operational importance for autocorrelated processes. Quality Technology & Quantitative Management 19:6, pages 665-691.
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K. Mabude, J.-C. Malela-Majika, P. Castagliola & S. C. Shongwe. (2022) Distribution-free mixed GWMA-CUSUM and CUSUM-GWMA Mann–Whitney charts to monitor unknown shifts in the process location. Communications in Statistics - Simulation and Computation 51:11, pages 6667-6690.
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Cang Wu & Shubin Si. (2022) Bivariate copula-based CUSUM charts for monitoring conditional nonlinear processes with first-order autocorrelation. Journal of Statistical Computation and Simulation 92:16, pages 3373-3399.
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Sandile Charles Shongwe, Jean-Claude Malela-Majika & Philippe Castagliola. (2021) The new synthetic and runs-rules schemes to monitor the process mean of autocorrelated observations with measurement errors. Communications in Statistics - Theory and Methods 50:24, pages 5806-5835.
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Sandile Charles Shongwe, Jean-Claude Malela-Majika & Philippe Castagliola. (2021) A combined mixed-s-skip sampling strategy to reduce the effect of autocorrelation on the X̄ scheme with and without measurement errors. Journal of Applied Statistics 48:7, pages 1243-1268.
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Sandile Charles Shongwe, Jean-Claude Malela-Majika, Philippe Castagliola & Thapelo Molahloe. (2020) Side-sensitive synthetic and runs-rules charts for monitoring AR(1) processes with skipping sampling strategies. Communications in Statistics - Theory and Methods 49:17, pages 4248-4269.
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Articles from other publishers (16)
Sangyeol Lee, Sangjo Lee & Chang Kyeom Kim. (2022) One‐class classification‐based monitoring for the mean and variance of time series. Quality and Reliability Engineering International 38:5, pages 2548-2565.
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Kung-Jeng Wang & Luh Juni Asrini. (2022) Deep learning-based automatic optical inspection system empowered by online multivariate autocorrelated process control. The International Journal of Advanced Manufacturing Technology 120:9-10, pages 6143-6162.
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Ishaq Adeyanju Raji, Muhammad Riaz, Mu'azu Ramat Abujiya, Nasir Abbas & Muhammad Hisyam Lee. (2021) Outliers’ detection with a sensitive exponentially weighted moving average control chart. Quality and Reliability Engineering International 38:4, pages 1790-1813.
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Carmen Patino-Rodriguez, Diana M. Pérez & Olga Usuga Manco. (2021)
Simulation approach for assessing the performance of the
γ
EWMA control chart
. International Journal of Quality & Reliability Management 38:9, pages 1902-1922.
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Zahid Rasheed, Hongying Zhang, Syed Masroor Anwar & Babar Zaman. (2021) Homogeneously Mixed Memory Charts with Application in the Substrate Production Process. Mathematical Problems in Engineering 2021, pages 1-15.
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Syed Masroor Anwar, Muhammad Aslam, Babar Zaman & Muhammad Riaz. (2021) Mixed memory control chart based on auxiliary information for simultaneously monitoring of process parameters: An application in glass field. Computers & Industrial Engineering 156, pages 107284.
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Sandile Charles Shongwe & Jean-Claude Malela-Majika. (2021) A new double sampling scheme to monitor the process mean of autocorrelated observations using an AR(1) model with a skip sampling strategy. Computers & Industrial Engineering 153, pages 107084.
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Sandile C. Shongwe, Jean‐Claude Malela‐Majika & Philippe Castagliola. (2020)
On monitoring the process mean of autocorrelated observations with measurement errors using the
w‐of‐w
runs‐rules scheme
. Quality and Reliability Engineering International 36:3, pages 1144-1160.
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Muhammad Aslam & Syed Masroor Anwar. (2020) An improved Bayesian Modified-EWMA location chart and its applications in mechanical and sport industry. PLOS ONE 15:2, pages e0229422.
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Saowanit Sukparungsee, Yupaporn Areepong & Rattikarn Taboran. (2020) Exponentially weighted moving average—Moving average charts for monitoring the process mean. PLOS ONE 15:2, pages e0228208.
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Chi‐Jui Huang, Shin‐Li Lu & Jen‐Hsiang Chen. (2019) Enhanced generally weighted moving average variance charts for monitoring process variance with individual observations. Quality and Reliability Engineering International 36:1, pages 285-302.
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Muhammad Abid, Hafiz Zafar Nazir, Muhammad Tahir, Muhammad Riaz & Tahir Abbas. (2020) A Comparative Analysis of Robust Dispersion Control Charts with Application Related to Health Care Data. Journal of Testing and Evaluation 48:1, pages 20180572.
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Maonatlala Thanwane, Sandile Charles Shongwe, Jean-Claude Malela-Majika & Muhammad Aslam. (2020) Parameter Estimation Effect of the Homogeneously Weighted Moving Average Chart to Monitor the Mean of Autocorrelated Observations With Measurement Errors. IEEE Access 8, pages 221352-221366.
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Muhammad Aslam, G. Rao, Ali AL-Marshadi & Chi-Hyuck Jun. (2019) A Nonparametric HEWMA-p Control Chart for Variance in Monitoring Processes. Symmetry 11:3, pages 356.
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Yung-Chia Chang, Kuei-Hu Chang, Mu-Chien Lee & Kuo-Hao Tsao. (2019) Using Exponentially Weighted Moving Average to Improve Buffer Adjustment of Demand-Driven Replenishment Strategies. Journal of Testing and Evaluation 47:1, pages 20170346.
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Nasir Abbas, Ishaq A. Raji, Muhammad Riaz & Khalid Al-Ghamdi. (2018) On Designing Mixed EWMA Dual-CUSUM Chart With Applications in Petro-Chemical Industry. IEEE Access 6, pages 78931-78946.
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