References
- Addeh, A., A. Khormali, and N. Golilarz. 2018. A. control chart pattern recognition using RBF neural network with new training algorithm and practical features. ISA Transactions 79:202–16.
- Agatonovic-Kustrin, S., and R. Beresford. 2000. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. Journal of Pharmaceutical and Biomedical Analysis 22 (5):717–27. doi: https://doi.org/10.1016/S0731-7085(99)00272-1.
- Consul, P. C. 1989. Generalized Poisson distributions: properties and applications. New York: Marcel Dekker.
- Esparza, A. O. Y., A. P. Alencar, and L. L. Ho. 2018. Effect of neglecting autocorrelation in regression EWMA charts for monitoring count time series. Quality and Reliability Engineering International 34 (8):1752–62. doi: https://doi.org/10.1002/qre.2367.
- Fan, J., C. Ma, and Y. Zhong. 2021. A selective overview of deep learning. Statistical Science : A Review Journal of the Institute of Mathematical Statistics 36 (2):264–90. doi: https://doi.org/10.1214/20-sts783.
- Farrell, M. H., T. Liang, and S. Misra. 2021. Deep neural networks for estimation and inference. Econometrica 89 (1):181–213. doi: https://doi.org/10.3982/ECTA16901.
- Fritsch, S. F. Günther, M. N. Wright, M. Suling, and S. M. Mueller. 2019. Training of neural networks; R Package, Neuralnet. Vienna, Austria: R Foundation for Statistical Computing.
- Goodfellow, I., Y. Bengio, and A. Courville. 2016. Deep learning. Cambridge, MA: MIT Press.
- Hassabis, D., D. Kumaran, C. Summerfield, and M. Botvinick. 2017. Neuroscience-inspired artificial intelligence. Neuron 95 (2):245–58. doi: https://doi.org/10.1016/j.neuron.2017.06.011.
- He, B., M. Xie, T. N. Goh, and K. L. Tsui. 2006. On control charts based on the generalized Poisson model. Quality Technology and Quantitative Management 3 (4):383–400. doi: https://doi.org/10.1080/16843703.2006.11673122.
- Hsu, H.-L., C.-K. Ing, T. L. Lai, and S.-H. Yu. 2018. Multistage manufacturing processes: Innovations in statistical modeling and inference. In Proceedings of the Pacific Rim Statistical Conference for Production Engineering; ICSA Book Series in Statistics; 67–84. Singapore: Springer Publisher.
- Jaggia, S., and S. Thosar. 1993. Multiple bids as a consequence of target management resistance: A count data approach. Review of Quantitative Finance and Accounting 3 (4):447–57. doi: https://doi.org/10.1007/BF02409622.
- Karatzoglou, A., A. Smola, K. Hornik, National ICT Australia, M. A. Maniscalco, and C. H. Teo. 2019. Kernel-based machine learning lab. R Package, kernlab. Vienna, Austria: R Foundation for Statistical Computing.
- Kim, J.-M. 2020. A review of copula methods for measuring uncertainty in finance and economics. Quantitative Bio-Science 39 (2):81–90.
- Kim, J.-M., and Ha, I. D. 2021. Deep learning-based residual control chart for binary response. Symmetry 13 (8):1389. doi: https://doi.org/10.3390/sym13081389.
- Kim, J.-M., Y. Liu, and N. Wang. 2020. Multi-stage change point detection with copula conditional distribution with PCA and functional PCA. Mathematics 8 (10):1777. doi: https://doi.org/10.3390/math8101777.
- Kim, J.-M., N. Wang, Y. Liu, and K. Park. 2020. Residual control chart for binary response with multicollinearity covariates by neural network model. Symmetry 12 (3):381. doi: https://doi.org/10.3390/sym12030381.
- Lambert, D. 1992. Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics 34 (1):1–14. doi: https://doi.org/10.2307/1269547.
- LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521 (7553):436–44. doi: https://doi.org/10.1038/nature14539.
- Li, L., K. Ota, and M. Dong. 2018. Deep learning for smart industry: Efficient manufacture inspection system with fog computing. IEEE Transactions on Industrial Informatics 14 (10):4665–73. doi: https://doi.org/10.1109/TII.2018.2842821.
- Maleki, M. R., A. Amiri, and P. Castagliola. 2018. An overview on recent profile monitoring papers (2008-2018) based on conceptual classification scheme. Computers & Industrial Engineering 126:705–28. doi: https://doi.org/10.1016/j.cie.2018.10.008.
- Maleki, M. R., P. Castagliola, A. Amiri, and M. B. C. Khoo. 2019. The effect of parameter estimation on phase II monitoring of poisson regression profiles. Communications in Statistics-Simulation and Computation 48 (7):1964–78. doi: https://doi.org/10.1080/03610918.2018.1429619.
- Masood, I., and A. Hassan. 2013. Pattern recognition for bivariate process mean shifts using feature-based artificial neural network. The International Journal of Advanced Manufacturing Technology 66 (9–12):1201–18. doi: https://doi.org/10.1007/s00170-012-4399-2.
- McCullagh, P., and J. A. Nelder. 1989. Generalized linear models. New York: Chapman and Hall.
- Montgomery, D. C. 2012. Statistical quality control. 7th ed. New York: John Wiley and Sons Press.
- Nelsen, R. B. 2006. An introduction to copulas. 2th ed. New York: Springer.
- Neubauer, A. S. 1997. The EWMA control chart: Properties and comparison with other quality-control procedures by computer simulation. Clinical Chemistry 43 (4):594–601. doi: https://doi.org/10.1093/clinchem/43.4.594.
- Park, K., J.-M. Kim, and D. Jung. 2018. GLM-based statistical control r-charts for dispersed count data with multicollinearity between input variables. Quality and Reliability Engineering International 34 (6):1103–9. doi: https://doi.org/10.1002/qre.2310.
- Park, K., J.-M. Kim, and D. Jung. 2020. Control charts based on randomized quantile residuals. Applied Stochastic Models in Business and Industry 36 (4):716–29. doi: https://doi.org/10.1002/asmb.2527.
- Polson, N. G., and V. Sokolov. 2017. Deep learning: A Bayesian perspective. Bayesian Analysis 12 (4):1275–304. doi: https://doi.org/10.1214/17-BA1082.
- Qi, D., Z. Wang, X. Zi, and Z. Li. 2016. Phase II monitoring of generalized linear profiles using weighted likelihood ratio charts. Computers & Industrial Engineering 94:178–87. doi: https://doi.org/10.1016/j.cie.2016.01.022.
- Qiu, P. 2013. Introduction to statistical process control. 1st ed. Boca Raton, FL: Chapman & Hall/CRC Texts in Statistical Science.
- Qiu, P., and L. You. 2018. Recent research in dynamic screening system for sequential process monitoring. In Proceedings of the Pacific Rim Statistical Conference for Production Engineering; ICSA Book Series in Statistics, 85–94. Singapore: Springer Publisher.
- Rai, R., M. K. Tiwari, D. Ivanov, and A. Dolgui. 2021. Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research 59 (16):4773–8. doi: https://doi.org/10.1080/00207543.2021.1956675.
- Schölkopf, B., A. Smola, and K.-R. Müller. 1998. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10 (5):1299–319. doi: https://doi.org/10.1162/089976698300017467.
- Sellers, K. F. 2012. A generalized statistical control chart for over- or under-dispersed data. Quality and Reliability Engineering International 28 (1):59–65. doi: https://doi.org/10.1002/qre.1215.
- Sellers, K. F., S. Borle, and G. Shmueli. 2012. The COM-Poisson model for count data: a survey of methods and applications. Applied Stochastic Models in Business and Industry 28 (2):104–16. doi: https://doi.org/10.1002/asmb.918.
- Shmueli, G., and H. Burkom. 2010. Statistical challenges facing early outbreak detection in biosurveillance. Technometrics 52 (1):39–51. doi: https://doi.org/10.1198/TECH.2010.06134.
- Skinner, K. R., D. C. Montgomery, and G. C. Runger. 2003. Process monitoring for multiple count data using generalized linear model-based control charts. International Journal of Production Research 41 (6):1167–80. doi: https://doi.org/10.1080/00207540210163964.
- Tran, M.-N., N. Nguyen, D. Nott, and R. Kohn. 2020. Bayesian deep net GLM and GLMM. Journal of Computational and Graphical Statistics 29 (1):97–113. doi: https://doi.org/10.1080/10618600.2019.1637747.
- Wang, J., Y. Ma, L. Zhang, R. X. Gao, and D. Wu. 2018. Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems 48:144–56. doi: https://doi.org/10.1016/j.jmsy.2018.01.003.
- Wang, B., F. Tao, X. Fang, C. Liu, Y. Liu, and T. Freiheit. 2021. Smart manufacturing and intelligent manufacturing: A comparative review. Engineering 7 (6):738–57. doi: https://doi.org/10.1016/j.eng.2020.07.017.
- Woodall, W. 2017. H. Bridging the gap between theory and practice in basic statistical process monitoring. Quality Engineering 29 (1):2–15.
- Woodall, W. H. 2007. Current research on profile monitoring. Production 17 (3):420–5. doi: https://doi.org/10.1590/S0103-65132007000300002.
- Woodall, W. H., and D. C. Montgomery. 2014. Some current directions in the theory and application of statistical process monitoring. Journal of Quality Technology 46 (1):78–94. doi: https://doi.org/10.1080/00224065.2014.11917955.
- Woodall, W. H., D. J. Spitzner, D. C. Montgomery, and G. Shilpa. 2004. Using control charts to monitor process and product quality profiles. Journal of Quality Technology 36 (3):309–20. doi: https://doi.org/10.1080/00224065.2004.11980276.
- Zan, T., Z. Liu, Z. Su, M. Wang, X. Gao, and D. Chen. 2019. Statistical process control with intelligence based on the deep learning model. Applied Sciences 10 (1):308. doi: https://doi.org/10.3390/app10010308.
- Zeileis, A., and C. Kleiber. 2013. countreg: Count data regression; R Package, countreg. Vienna, Austria: R Foundation for Statistical Computing.