How to cite this paper
Thanwane, M., Shongwe, S., Aslam, M., Malela-Majika, J & Albassam, M. (2021). A homogenously weighted moving average scheme for observations under the effect of serial dependence and measurement inaccuracy.International Journal of Industrial Engineering Computations , 12(4), 401-414.
Refrences
Abbas, N. (2020). Homogeneously weighted moving average control chart with an application in substrate manufacturing process. Computers & Industrial Engineering, 120, 460-470.
Abbas, N., Riaz, M., Ahmad, S., Abid, M., & Zaman, B. (2020). On the efficient monitoring of multivariate processes with unknown parameters. Mathematics, 8(5), 823.
Abid, M., Shabbir, A., Nazir, H.Z., Sherwani, R.A.K., & Riaz, M. (2020a). A double homogeneously weighted moving average control chart for monitoring of the process mean. Quality and Reliability Engineering International, 36(5), 1513-1527.
Abid, M., Mei, S., Nazir, H.Z., Riaz, M., & Hussain, S. (2020b). A mixed HWMA-CUSUM mean chart with an application to manufacturing process. Quality and Reliability Engineering International, 37(2), 618-631.
Adegoke, N.A., Smith, A.N.H., Anderson, M.J., Sanusi, R.A., & Pawley, M.D.M. (2019a). Efficient homogeneously weighted moving average chart for monitoring process mean using an auxiliary variable. IEEE Access, 7, 94021-94032.
Adegoke, N.A., Abbasi, S.A., Smith, A.N.H., Anderson, M.J., & Pawley, M.D.M. (2019b). A multivariate homogeneously weighted moving average control chart. IEEE Access, 7, 9586-9597.
Adeoti, O.A., & Koleoso, S.O. (2020). A hybrid homogeneously weighted moving average control chart for process monitoring. Quality and Reliability Engineering International, 36(6), 2170-2186.
Ahmad, S., Riaz, M., Hussain, S., & Abbasi, S.A. (2019). On auxiliary information-based control charts for autocorrelated processes with application in manufacturing industry. The International Journal of Advanced Manufacturing Technology, 100(5-8), 1965-1980.
Alevizakos, V., Chatterjee, K., & Koukouvinos, C. (2021). The extended homogenously weighted moving average control chart. Quality and Reliability Engineering International, DOI: 10.1002/qre.2849
Alwan, L.C., & Radson, D. (1992). Time-series investigation of subsample mean chart. IIE Transactions, 24(5), 66-80.
Arif, F., Noor-ul-Amin, M., & Hanif, M. (2020). Joint monitoring of mean and variance using likelihood ratio test statistic with measurement error. Quality Technology & Quantitative Management, 18(2), 202-224.
Asif, F., Khan, S., & Noor-ul-Amin, M. (2020). Hybrid exponentially weighted moving average control chart with measurement error. Iranian Journal of Science and Technology, Transactions A: Science, 44(3), 801-811.
Aslam, M., Saghir, A., & Ahmad, L. (2020). Introduction to Statistical Process Control, Hoboken, NJ, USA: Wiley.
Costa, A.F.B., & Castagliola, P. (2011). Effect of measurement error and autocorrelation on the X ̅ chart. Journal of Applied Statistics, 38(4), 661-673.
Dargopatil, P., & Ghute, V. (2019). New sampling strategies to reduce the effect of autocorrelation on the synthetic T2 chart to monitor bivariate process. Quality and Reliability Engineering International, 35(1), 30-46.
Dawod, A., Adegoke, N.A., & Abbasi, S.A. (2020). Efficient linear profile schemes for monitoring bivariate correlated processes with applications in the pharmaceutical industry. Chemometrics and Laboratory Systems, 206, 104137.
Franco, B.C., Castagliola, P., Celano, G., & Costa, A.F.B. (2014). A new sampling strategy to reduce the effect of autocorrelation on a control chart. Journal of Applied Statistics, 41(7), 1408-1421.
Linna, K.W., & Woodall, W.H. (2001). Effect of measurement error on Shewhart control charts. Journal of Quality Technology, 33(2), 213-222.
Maleki, M.R., Amiri, A., & Castagliola, P. (2017). Measurement errors in statistical process monitoring: A literature review. Computers & Industrial Engineering, 103, 316-329.
Malela-Majika, J.-C., Shongwe, S.C., & Adeoti, O.A. (2021). A hybrid homogenously weighted moving average control chart for process monitoring: Discussion. Quality and Reliability Engineering International, DOI: 10.1002/qre.2911.
Maravelakis, P., Panaretos, J., & Psarakis, S. (2004). EWMA chart and measurement error. Journal of Applied Statistics, 31(4), 445-455.
Maravelakis, P. (2012). Measurement error on the CUSUM control chart. Journal of Applied Statistics, 39(2), 323-336.
Montgomery, D.C. (2013). Statistical Quality Control: A Modern Introduction, 7th ed., Singapore: John Wiley & Sons.
Nawaz, T., & Han, D. (2020). Monitoring the process location by using new ranked set sampling based memory control charts. Quality Technology & Quantitative Management, 17(3), 255-284.
Nguyen, H.D., Nguyen, Q.T., Nguyen, T.H., Balakrishnan, N., & Tran, K.P. (2020). The performance of the EWMA median chart in the presence of measurement error. Artificial Intelligence Evolution, 1(1), 48-62.
Oh, J., & Weiβ, C.H. (2020). On the individuals chart with supplementary runs rules under serial dependence. Methodology and Computing in Applied Probability, 22, 1257-1273.
Prajapati, D.R., & Singh, S. (2012). Control charts for monitoring the autocorrelated process parameters: A literature review. International Journal of Productivity and Quality Management, 10(2), 207-249.
Raza, M., Nawaz, T., & Han, D. (2020). On designing distribution-free homogeneously weighted moving average control charts. Journal Testing and Evaluation, 48(4), 3154-3171.
Shongwe, S.C., Malela-Majika, J.-C., & Castagliola, P. (2020). The new synthetic and runs-rules schemes to monitor the process mean of autocorrelated observations with measurement errors. Communications in Statistics – Theory and Methods, DOI: 10.1080/03610926.2020.1737125.
Shongwe, S.C., & Malela-Majika, J.-C. (2020). A new variable sampling size and interval synthetic and runs-rules schemes to monitor the process mean of autocorrelated observations with measurement errors. International Journal of Industrial Engineering Computations, 11(4), 607-626.
Shongwe, S.C., Malela-Majika, J.-C., & Castagliola, P. (2021). A combined mixed-s-skip sampling strategy to reduce the effect of autocorrelation on the X ̅ scheme with and without measurement errors. Journal of Applied Statistics, 48(7), 1243-1268.
Thanwane, M., Malela-Majika, J.-C., Castagliola, P., & Shongwe, S.C. (2020). The effect of measurement errors on the performance of the homogeneously weighted moving average X ̅ monitoring scheme. Transactions of the Institute of Measurement and Control, 43(3), 728-745.
Xiaohong, L., & Zhaojun, W. (2009). The CUSUM control chart for the autocorrelated data with measurement error. Chinese Journal of Applied Probability, 25(5), 461-474.
Yang, S.F., & Yang, C.M. (2005). Effects of imprecise measurement on the two dependent processes control for the autocorrelated observations. The International Journal of Advanced Manufacturing Technology, 26(5-6), 623-630.
Zaidi, F., Castagliola, P., Tran, K.P., & Khoo, M.B.C. (2020). Performance of the MEWMA-CoDa control chart in the presence of measurement errors. Quality and Reliability Engineering International, 36(7), 2411-2440.
Abbas, N., Riaz, M., Ahmad, S., Abid, M., & Zaman, B. (2020). On the efficient monitoring of multivariate processes with unknown parameters. Mathematics, 8(5), 823.
Abid, M., Shabbir, A., Nazir, H.Z., Sherwani, R.A.K., & Riaz, M. (2020a). A double homogeneously weighted moving average control chart for monitoring of the process mean. Quality and Reliability Engineering International, 36(5), 1513-1527.
Abid, M., Mei, S., Nazir, H.Z., Riaz, M., & Hussain, S. (2020b). A mixed HWMA-CUSUM mean chart with an application to manufacturing process. Quality and Reliability Engineering International, 37(2), 618-631.
Adegoke, N.A., Smith, A.N.H., Anderson, M.J., Sanusi, R.A., & Pawley, M.D.M. (2019a). Efficient homogeneously weighted moving average chart for monitoring process mean using an auxiliary variable. IEEE Access, 7, 94021-94032.
Adegoke, N.A., Abbasi, S.A., Smith, A.N.H., Anderson, M.J., & Pawley, M.D.M. (2019b). A multivariate homogeneously weighted moving average control chart. IEEE Access, 7, 9586-9597.
Adeoti, O.A., & Koleoso, S.O. (2020). A hybrid homogeneously weighted moving average control chart for process monitoring. Quality and Reliability Engineering International, 36(6), 2170-2186.
Ahmad, S., Riaz, M., Hussain, S., & Abbasi, S.A. (2019). On auxiliary information-based control charts for autocorrelated processes with application in manufacturing industry. The International Journal of Advanced Manufacturing Technology, 100(5-8), 1965-1980.
Alevizakos, V., Chatterjee, K., & Koukouvinos, C. (2021). The extended homogenously weighted moving average control chart. Quality and Reliability Engineering International, DOI: 10.1002/qre.2849
Alwan, L.C., & Radson, D. (1992). Time-series investigation of subsample mean chart. IIE Transactions, 24(5), 66-80.
Arif, F., Noor-ul-Amin, M., & Hanif, M. (2020). Joint monitoring of mean and variance using likelihood ratio test statistic with measurement error. Quality Technology & Quantitative Management, 18(2), 202-224.
Asif, F., Khan, S., & Noor-ul-Amin, M. (2020). Hybrid exponentially weighted moving average control chart with measurement error. Iranian Journal of Science and Technology, Transactions A: Science, 44(3), 801-811.
Aslam, M., Saghir, A., & Ahmad, L. (2020). Introduction to Statistical Process Control, Hoboken, NJ, USA: Wiley.
Costa, A.F.B., & Castagliola, P. (2011). Effect of measurement error and autocorrelation on the X ̅ chart. Journal of Applied Statistics, 38(4), 661-673.
Dargopatil, P., & Ghute, V. (2019). New sampling strategies to reduce the effect of autocorrelation on the synthetic T2 chart to monitor bivariate process. Quality and Reliability Engineering International, 35(1), 30-46.
Dawod, A., Adegoke, N.A., & Abbasi, S.A. (2020). Efficient linear profile schemes for monitoring bivariate correlated processes with applications in the pharmaceutical industry. Chemometrics and Laboratory Systems, 206, 104137.
Franco, B.C., Castagliola, P., Celano, G., & Costa, A.F.B. (2014). A new sampling strategy to reduce the effect of autocorrelation on a control chart. Journal of Applied Statistics, 41(7), 1408-1421.
Linna, K.W., & Woodall, W.H. (2001). Effect of measurement error on Shewhart control charts. Journal of Quality Technology, 33(2), 213-222.
Maleki, M.R., Amiri, A., & Castagliola, P. (2017). Measurement errors in statistical process monitoring: A literature review. Computers & Industrial Engineering, 103, 316-329.
Malela-Majika, J.-C., Shongwe, S.C., & Adeoti, O.A. (2021). A hybrid homogenously weighted moving average control chart for process monitoring: Discussion. Quality and Reliability Engineering International, DOI: 10.1002/qre.2911.
Maravelakis, P., Panaretos, J., & Psarakis, S. (2004). EWMA chart and measurement error. Journal of Applied Statistics, 31(4), 445-455.
Maravelakis, P. (2012). Measurement error on the CUSUM control chart. Journal of Applied Statistics, 39(2), 323-336.
Montgomery, D.C. (2013). Statistical Quality Control: A Modern Introduction, 7th ed., Singapore: John Wiley & Sons.
Nawaz, T., & Han, D. (2020). Monitoring the process location by using new ranked set sampling based memory control charts. Quality Technology & Quantitative Management, 17(3), 255-284.
Nguyen, H.D., Nguyen, Q.T., Nguyen, T.H., Balakrishnan, N., & Tran, K.P. (2020). The performance of the EWMA median chart in the presence of measurement error. Artificial Intelligence Evolution, 1(1), 48-62.
Oh, J., & Weiβ, C.H. (2020). On the individuals chart with supplementary runs rules under serial dependence. Methodology and Computing in Applied Probability, 22, 1257-1273.
Prajapati, D.R., & Singh, S. (2012). Control charts for monitoring the autocorrelated process parameters: A literature review. International Journal of Productivity and Quality Management, 10(2), 207-249.
Raza, M., Nawaz, T., & Han, D. (2020). On designing distribution-free homogeneously weighted moving average control charts. Journal Testing and Evaluation, 48(4), 3154-3171.
Shongwe, S.C., Malela-Majika, J.-C., & Castagliola, P. (2020). The new synthetic and runs-rules schemes to monitor the process mean of autocorrelated observations with measurement errors. Communications in Statistics – Theory and Methods, DOI: 10.1080/03610926.2020.1737125.
Shongwe, S.C., & Malela-Majika, J.-C. (2020). A new variable sampling size and interval synthetic and runs-rules schemes to monitor the process mean of autocorrelated observations with measurement errors. International Journal of Industrial Engineering Computations, 11(4), 607-626.
Shongwe, S.C., Malela-Majika, J.-C., & Castagliola, P. (2021). A combined mixed-s-skip sampling strategy to reduce the effect of autocorrelation on the X ̅ scheme with and without measurement errors. Journal of Applied Statistics, 48(7), 1243-1268.
Thanwane, M., Malela-Majika, J.-C., Castagliola, P., & Shongwe, S.C. (2020). The effect of measurement errors on the performance of the homogeneously weighted moving average X ̅ monitoring scheme. Transactions of the Institute of Measurement and Control, 43(3), 728-745.
Xiaohong, L., & Zhaojun, W. (2009). The CUSUM control chart for the autocorrelated data with measurement error. Chinese Journal of Applied Probability, 25(5), 461-474.
Yang, S.F., & Yang, C.M. (2005). Effects of imprecise measurement on the two dependent processes control for the autocorrelated observations. The International Journal of Advanced Manufacturing Technology, 26(5-6), 623-630.
Zaidi, F., Castagliola, P., Tran, K.P., & Khoo, M.B.C. (2020). Performance of the MEWMA-CoDa control chart in the presence of measurement errors. Quality and Reliability Engineering International, 36(7), 2411-2440.