A theoretical framework and efficiency study of multivariate statistical process control charts

References

• Anderson , T. W. 1984 . An Introduction to Multivariate Statistical Analysis, , second edn , New York , NY : Wiley .
   Google Scholar
• Boros , E. , Hammer , P. L. and Tavares , G. 2005 . Local search heuristics for unconstrained quadratic binary optimization http://rutcor.rutgers.edu/pub/rrr/reports2005/9_2005.ps RUTCOR Technical Report. Available at
   Google Scholar
• Casella , G. and Berger , R. 1990 . Statistical Inference , Belmont , CA : Duxbury .
   Google Scholar
• Doganaksoy , N. , Faltin , F. W. and Tucker , W. T. 1991 . Identification of out of control quality characteristics in a multivariate manufacturing environment . Communications in Statistics – Theory and Methods , A20 ( 9 ) : 2775 – 2790 .
   Web of Science ®Google Scholar
• Flury , B. and Riedwyl , H. 1988 . Multivariate Statistics: A Practical Approach , London , UK : Chapman & Hall .
   Google Scholar
• Hawkins , D. M. 1991 . Multivariate quality control using regression-adjusted variables . Technometrics , 33 : 61 – 75 .
   Web of Science ®Google Scholar
• Hayter , A. J. and Tsui , K.-L. 1994 . Identification and quantification in multivariate quality control problems . Journal of Quality Technology , 26 : 197 – 208 .
   Web of Science ®Google Scholar
• Healy , J. D. 1987 . A note on multivariate CUSUM procedures . Technometrics , 29 : 409 – 412 .
   Web of Science ®Google Scholar
• Hotelling , H. 1947 . “ Multivariate quality control ” . In Techniques of Statistical Analysis , Edited by: Eisenhart , C. , Hastay , M. W. and Wallis , W. A. 111 – 184 . New York , NY : McGraw-Hill .
   Google Scholar
• Jackson , J. E. 1985 . Multivariate quality control . Communications in Statistics – Theory and Methods , 14 ( 11 ) : 2657 – 2688 .
   Web of Science ®Google Scholar
• Lehmann , E. L. 1986 . Testing Statistical Hypotheses , 2nd ed. , New York , NY : J. Wiley .
   Google Scholar
• Li , J. and Shi , J. 2007 . Knowledge discovery from observational data for process control through causal Bayesian networks . IIE Transactions, Web , 39 : 681 – 690 .
   Web of Science ®Google Scholar
• Liu , H. C. , Jiang , W. , Tangirala , A. and Shah , S. L. 2006 . An adaptive regression adjusted monitoring and diagnosis scheme . Journal of Chemometrics , 20 : 280 – 293 .
   Web of Science ®Google Scholar
• Lowry , C. A. and Montgomery , D. C. 1995 . A review of multivariate control charts . IIE Transactions , 27 : 800 – 810 .
   Web of Science ®Google Scholar
• Mardia , K. V. , Kent , J. T. and Bibby , J. M. 1979 . Multivariate Analysis , New York , NY : Academic Press .
   Google Scholar
• Mason , R. L. , Tracy , N. D. and Young , J. C. 1995 . Decomposition of T 2 for multivariate control chart interpretation . Journal of Quality Technology , 27 : 99 – 108 .
   Web of Science ®Google Scholar
• Mason , R. L. , Tracy , N. D. and Young , J. C. 1997 . A practical approach for interpreting multivariate t 2 control chart signals . Journal of Quality Technology , 29 : 396 – 406 .
   Web of Science ®Google Scholar
• Pelikan , M. 2005 . Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms , New York , NY : Springer-Verlag .
   Google Scholar
• Runger , G. C. 1996 . Projections and the U 2 multivariate control chart . Journal of Quality Technology , 28 : 313 – 319 .
   Web of Science ®Google Scholar
• Timm , N. H. 1996 . Multivariate quality control using finite intersection tests . Journal of Quality Technology , 28 : 233 – 243 .
   Web of Science ®Google Scholar
• Tsui , K.-L. and Woodall , W. H. 1993 . Multivariate control charts based on loss functions . Sequential Analysis , 12 : 79 – 92 .
   Google Scholar
• Tracy , N. D. , Young , J. C. and Mason , R. L. 1992 . Multivariate control charts for individual observations . Journal of Quality Technology , 24 ( 2 ) : 88 – 95 .
   Web of Science ®Google Scholar
• Woodall , W. H. 2000 . Controversies and contradictions in statistical process control . Journal of Quality Technology , 32 : 341 – 350 .
   Web of Science ®Google Scholar