Discussion on “Recent Advances in Process Monitoring: Nonparametric and Variable-Selection Methods for Phase I and Phase II”

REFERENCES

• Albers, W., Kallenberg W. C. M. (2004). Estimation in Shewhart control charts: Effects andcorrections. Metrika, 59:207–234.
   Web of Science ®Google Scholar
• Albers, W., Kallenberg W. C. M. (2007). Shewhart control charts in new perspective. Sequential Analysis, 26:123–151.
   Google Scholar
• Antoch, J. Hušková, M. (2001). Permutation tests in change point analysis. Statistics & Probability Letters, 53:37–46.
   Web of Science ®Google Scholar
• Bai, J. (1999). Likelihood ratio tests for multiple structural changes. Journal of Econometrics, 91:299–323.
   Web of Science ®Google Scholar
• Bodnar, O. Schmid, W. (2007). Surveillance of the mean behaviour ofmultivariate time series. Statistica Neerlandica, 61:383–406.
   Web of Science ®Google Scholar
• Bodnar, O. Schmid, W. (2009). Discussion on “Optimal sequential surveillance for finance, public health, and other areas” by Marianne Frisén. Sequential Analysis, 28:381–385.
   Google Scholar
• Box, G. E. P. Tiao, G. C. (1975). Intervention analysis with applications to economic and environmental problems. Journal of the American Statistical Association, 70:70–79.
   Web of Science ®Google Scholar
• Brodsky, B.E. Darkhovsky, B. S. (1993). Nonparametric Methods in Change Point Problems. Dordrecht, The Netherlands: Kluwer.
   Google Scholar
• Capizzi, G. (2015). Recent advances in process monitoring: semi parametric and variable selection methods for Phase I and Phase II. Quality Engineering, 27: 44–67.
   Web of Science ®Google Scholar
• Capizzi, G. Masarotto, G. (2011). A least angle regression control chart for multidimensional data. Technometrics, 53:285–296.
   Web of Science ®Google Scholar
• Capizzi, G. Masarotto, G. (2013). Phase I distribution-free analysis of univariate data. Journal of Quality Technology, 45:273–284.
   Web of Science ®Google Scholar
• Chen, J. Gupta, A. K. (2012). Parametric Statistical Change Point Analysis. New York: Birkhäuser.
   Google Scholar
• Coles, S. (2001). An Introduction to Statistical Modeling of Extreme Values. New York: Springer.
   Google Scholar
• Csörgö, M. Horváth, L. (1997). Limit Theorems in Change-Point Analysis. New Jersey: Wiley.
   Google Scholar
• Efron, E., Hastie, T., Johnstone, I., Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32:407–499.
   Web of Science ®Google Scholar
• Erdmann, C. Emerson, J. W. (2007). An R package for performing a Bayesian analysis of change point problems. Journal of Statistical Software, 23:1–13.
   Web of Science ®Google Scholar
• Flak, T., Schmid, W., Sigmund, R. (1996). A comparison of several procedures for identifying outliers in contaminated ARMA processes. Computational Statistics, 11:175–195.
   Web of Science ®Google Scholar
• Good, P., (2005). Permutation, Parametric, and Bootstrap Tests of Hypotheses. New York: Springer.
   Google Scholar
• Gibbons, J. D. Chakraborti, S. (2011). Nonparametric Statistical Inference. Boca Raton, FL: CRC Press
   Google Scholar
• Hansen, B. E. (2000). Testing for structural change in conditional models. Journal of Econometrics, 97:93–115.
   Web of Science ®Google Scholar
• Jensen, W. A., Jones-Farmer, L. A., Champ, C. W., Woodall, W. H. (2006). Effects of parameter estimation on control chart properties: A literature review. Journal of Quality Technology, 38:349–364.
   Web of Science ®Google Scholar
• Jiang, W., Wang, K., Tsung, F. (2012). A variable-selection-based multivariate EWMA chart for process monitoring and diagnosis. Journal of Quality Technology, 44:209–230.
   Web of Science ®Google Scholar
• Karian, Z. A. Dudewicz, E. J. (2000). Fitting Statistical Distributions. The Generalized Lambda Distribution and Generalized Bootstrap Methods. Boca Raton, FL: CRC Press.
   Google Scholar
• Killick, R. Eckley, I. A. (2014). Changepoint: An R Package for changepoint analysis. Journal of Statistical Software, 58(3):1–19.
   Web of Science ®Google Scholar
• Killick, R., Fearnhead, P., Eckley, I. A. (2012). Optimal detection of change points with a linear computational cost. Journal of the American Statistical Association, 107:1590–1598.
   Web of Science ®Google Scholar
• Kramer, H. Schmid, W. (1997). EWMA charts for multivariate time series. Sequential Analysis, 16:131–154.
   Google Scholar
• Li, B., Wang, K., Yeh, A. B. (2013). Monitoring the covariance matrix via penalized likelihood estimation. IIE Transactions, 45:132–146.
   Web of Science ®Google Scholar
• Maronna, R. A., Martin, D. R., Yohai, V. J. (2006). Robust Statistics: Theory and Methods. New Jersey: Wiley.
   Google Scholar
• Perron, P., (2006). Dealing with Structural Breaks. In: Patterson, P., and Mills, T. C. Eds. Palgrave Handbook of Econometrics, Vol. 1. Econometric Theory. Basingstoke: Palgrave Macmillan, 278–352.
   Google Scholar
• Slifker, J. F., Shapiro, S. S. (1980). The Johnson family: Selection and parameter estimation. Technometrics, 22:239–246.
   Web of Science ®Google Scholar
• Wang, K., Jiang, W. (2009). High-dimensional process monitoring and fault isolation via variable selection. Journal of Quality Technology, 41:247–258.
   Web of Science ®Google Scholar
• Wei, W. W. S. (2005). Time Series Analysis: Univariate and Multivariate Methods. New Jersey: Pearson.
   Google Scholar
• Wilcox, R. R. (2012). Introduction to Robust Estimation and Hypothesis Testing. Waltham: Academic Press.
   Google Scholar
• Zou, C., Ning, X., Tsung, F. (2010). LASSO-based multivariate linear profile monitoring. Annals of Operation Research, 192:3–19.
   Web of Science ®Google Scholar
• Zou, C. Qiu, P. (2009). Multivariate statistical process control using LASSO. Journalof the American Statistical Association, 104:1586–1596.
   Web of Science ®Google Scholar