Monitoring individuals with irregular semiparametric longitudinal behaviour

References

• Apley, D. W., & Lee, H. C. (2003). Design of exponentially weighted moving average control charts for autocorrelated processes with model uncertainty. Technometrics, 45, 187–198.
   Web of Science ®Google Scholar
• DeLeval, M. R., Francois, K., Bull, C., Brawn, W. B., & Spiegelhalter, D. J. (1994). Analysis of a cluster of surgical failures. Journal of Thoracic & Cardiovascular Surgery, 104, 914–924.
   Google Scholar
• Ding, Y., Zeng, L., & Zhou, S. (2006). Phase I analysis for monitoring nonlinear profiles in manufacturing processes. Journal of Quality Technology, 38, 199–216.
   Web of Science ®Google Scholar
• Fan, J., & Gijbels, I. (1996). Local polynomial modeling and its applications. London: Chapman and Hall.
   Google Scholar
• Graham, M. A., Mukherjee, A., & Chakraborti, S. (2012). Distribution-free exponentially weighted moving average control charts for monitoring unknown location. Computational Statistics & Data Analysis, 56(8), 2539–2561.
   Web of Science ®Google Scholar
• Grigg, O., & Farewell, V. (2004). An overview of risk-adjusted charts. Journal of the Royal Statistical Society, 167, 523–539.
   Google Scholar
• Hawkins, D. M., & Olwell, D. H. (1998). Cumulative sum charts and charting for quality improvement. New York: Springer.
   Google Scholar
• Hawkins, D. M., Qiu, P., & Chang, W. K. (2003). The changepoint model for statistical process control. Journal of Quality Technology, 35, 355–366.
   Web of Science ®Google Scholar
• Jensen, W. A., & Birch, J. B. (2009). Profile monitoring via nonlinear mixed models. Journal of Quality Technology, 41, 18–34.
   Web of Science ®Google Scholar
• Lin, X., & Carroll, R. (2001). Semiparametric regression for clustered data using generalized estimating equations. Journal of the American Statistical Association, 96, 1045–1056.
   Web of Science ®Google Scholar
• Li, J., & Qiu, P. (2016). Nonparametric Dynamic Screening System for Monitoring Correlated Longitudinal Data. IIE Transactions, 772–786.
   Google Scholar
• Li, Z., Xie, M., & Zhou, M. (2016). Rank-based EWMA procedure for sequentially detecting changes of process location and variability. Quality Technology and Quantitative Management, 1–20.
   Google Scholar
• Lovegrove, J., Sherlaw-Johnson, C., Valencia, O., Treasure, T., & Gallivan, S. (1999). Monitoring the performance of cardiac surgeons. Journal of the Operational Research Society, 50, 684–689.
   Web of Science ®Google Scholar
• Lu, C. W., & Reynolds, M. R., Jr (2001). Control charts for monitoring an autocorrelated process. Journal of Quality Technology, 33, 316–334.
   Web of Science ®Google Scholar
• Montgomery, D. C. (2009). Introduction to statistical quality control (6th ed.). New York: John Wiley and Sons.
   Google Scholar
• Mukherjee, A., Graham, M., & Chakraborti, S. (2014). Design and implementation of cusum exceedance control charts for unknown location. International Journal of Production Research, 52, 5546–5564.
   Web of Science ®Google Scholar
• Nadaraya, E. A. (1964). On Estimating regression. Theory of Probability and its Applications, 9, 141–142.
   Google Scholar
• Page, E. S. (1954). Continuous inspection scheme. Biometrika, 41, 100–114.
   Web of Science ®Google Scholar
• Poloniecki, J., Valencia, O., & Littlejohns, P. (1998). Cumulative risk adjusted mortality chart for detecting changes in death rate: Observational study of heart surgery. Bmj Clinical Research, 316, 1697–1700.
   PubMed Web of Science ®Google Scholar
• Qiu, P. (2014). Introduction to Statistical Process Control. Boca Raton, FL: Chapman and Hall/CRC Press.
   Google Scholar
• Qiu, P., & Xiang, D. (2014). Univariate dynamic screening system: An approach for identifying individuals with irregular longitudinal behavior. Technometrics, 56, 248–260.
   Web of Science ®Google Scholar
• Qiu, P., & Xiang, D. (2015). Surveillance of cardiovascular diseases using a multivariate dynamic screening system. Statistics in Medicine, 34, 2204–2221.
   PubMed Web of Science ®Google Scholar
• Roberts, S. W. (1959). Control Chart Tests Based on Geometric Moving Averages. Technometrics, 1, 239–250.
   Google Scholar
• Runger, G. C., & Willemain, T. R. (1996). Batch means control charts for autocorrelated data. IIE Transactions, 28, 483–487.
   Web of Science ®Google Scholar
• Severini, T. A., & Staniswalis, J. G. (1994). Quasi-likelihood estimation in semiparametric models. Journal of the American Statistical Association, 89, 501–511.
   Web of Science ®Google Scholar
• Shewhart, W. A. (1931). Economic control of quality of manufactured product. New York: D. Van Nostrand Company.
   Google Scholar
• Spiegelhalter, D. J., Grigg, O. A., Kinsman, R., & Treasure, T. (2003). Sequential probability ratio tests (sprts) for monitoring risk-adjusted outcomes. International Journal for Quality in Health Care, 15, 1–7.
   Google Scholar
• Steiner, S. H., Cook, R. J., Farewell, V. T., & Treasure, T. (2000). Monitoring surgical performance using risk-adjusted cumulative sum charts. Biostatistics, 1, 441–452.
   PubMedGoogle Scholar
• Steiner, S. H., & Jones, M. (2010). Risk-adjusted survival time monitoring with an updating exponentially weighted moving average (ewma) control chart. Statistics in Medicine, 29, 444–54.
   PubMed Web of Science ®Google Scholar
• Watson, G. S. (1964). Smooth regression analysis. Sankhyā: The Indian Journal of Statistics. Series A, 26, 359–372.
   Google Scholar
• Woodall, W. H. (2006). The use of control charts in health-care and public-health surveillance. Quality Control & Applied Statistics, 52, 253–256.
   Google Scholar
• Zeger, S. L., & Diggle, P. J. (1994). Semi-Parametric Models for Longitudinal Data With Application to CD4 Cell Numbers in HIV Seroconverters. Biometrics, 50, 689–699.
   PubMed Web of Science ®Google Scholar
• Zhang, D., Lin, X., Raz, J., & Sowers, M. (1998). Semi-Parametric Stochastic Mixed Models for Longitudinal Data. Journal of the American Statistical Association, 93, 710–719.
   Web of Science ®Google Scholar
• Zou, C., Wang, Z., & Tsung, F. (2012). A spatial rank-based multivariate EWMA control chart. Naval Research Logistics, 59, 91–110.
   Web of Science ®Google Scholar