Adaptive CUSUM procedures with EWMA-based shift estimators

IIE Transactions (Institute of Industrial Engineers)

Volumn 40, Issue 10, 2008, Pages 992-1003

  Jiang, Wei ^a   Shu, Lianjie ^b   Apley, Daniel W ^c  

^a STEVENS INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF MACAU   (Macao)

^c Northwestern University   (United States)

Author keywords

Average run length; Change point detection; EWMA; Markov chain

Indexed keywords

GRAPHIC METHODS; MAXIMUM LIKELIHOOD ESTIMATION; VECTOR QUANTIZATION;

AVERAGE RUN LENGTH; BASIC IDEA; CHANGE-POINT DETECTION; CUMULATIVE SUM CHARTS; DETECTION PERFORMANCES; EWMA; EXPONENTIALLY WEIGHTED MOVING AVERAGE; LIKELIHOOD RATIO TESTING; MARKOV CHAIN; MEAN SHIFT; MEAN SHIFTS; REFERENCE VALUES; RUN-LENGTH PERFORMANCE; WEIGHTING FUNCTIONS;

ESTIMATION;

EID: 49249101687     PISSN: 0740817X     EISSN: 15458830     Source Type: Journal    
DOI: 10.1080/07408170801961412     Document Type: Article

References (26)

1. Banzal, R. K. and Papantoni-Kazakos, P. (1986) An algorithm for detecting a change in stochastic process. IEEE Transactions on Information Theory, 32, pp. 227-235.
2. Barnard, G. (1959) Control charts and stochastic processes. Journal of the Royal Statistical Society, Ser B, 21, pp. 239-257.
3. Basseville, M. and Nikiforov, I. (1993) Detection of Abrupt Changes, Prentice-Hall, Englewood Cliffs, NJ
4. Brook, D. and Evans, D. A. (1972) An approach to the probability distribution of CUSUM run length. Biometrika, 59, pp. 539-549.
5. Capizzi, G. and Masarotto, G. (2003) An adaptive exponentially weighted moving average control chart. Technometrics, 45, pp. 199-207.
6. Chen, A. and Elsayed, E. A. (2000) An alternative mean estimator for process monitored by SPC charts. International Journal of Production Research, 38, pp. 3093-3109.
7. Chen, A. and Elsayed, E. A. (2002) Design and performance analysis of the exponentially weighted moving average mean estimate for process subject to random step changes. Technometrics, 44, pp. 379-389.
8. Chernoff, H. and Zacks, S. (1964) Estimating the current mean of a normal distribution which is subjected to changes in time. Annals of Mathematical Statistics, 35, pp. 999-1018.
9. Jones, L. A., Champ, C. W. and Rigdon, S. E. (2001) The performance of exponentially weighted moving average charts with estimated parameters. Technometrics, 43, pp. 156-167.
10. Lorden, G. (1971) Procedures for reacting to a change in distribution. Annals of Mathematical Statistics, 42, pp. 1897-1908.
11. Lorden, G. and Eisenberger, I. (1973) Detection of failure rate increases. Technometrics, 15, pp. 167-175.
12. Lucas, J. M. (1982) Combined Shewhart-CUSUM quality control schemes. Journal of Quality Technology, 14, pp. 51-59.
13. Lucas, J. M. and Crosier, R. B. (1982) Fast initial response for CUSUM quality-control schemes: Give your CUSUM a head start. Technometrics, 24, pp. 199-205.
14. Lucas, J. M. and Saccucci, M. S. (1990) Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics, 32:1, pp. 1-12.
15. Montgomery, D. C. (2005) Introduction to Statistical Quality Control,, Wiley, New York, NY
16. Moustakides, G. V. (1986) Optimal stopping times for detecting changes in distributions. Annals of Statistics, 14, pp. 1379-1387.
17. Page, E. S. (1954) Continuous inspection schemes. Biometrika, 42, pp. 243-254.
18. Pollak, M. (1985) Optimal detection of a change in distribution. Annals of Statistics, 13, pp. 206-227.
19. Roberts, S. W. (1959) Control chart tests based on geometric moving averages. Technometrics, 1, pp. 239-250.
20. Shu, L. and Jiang, W. (2006) A Markov chain model for the adaptive CUSUM control chart. Journal of Quality Technology, 38, pp. 135-147.
21. Siegmund, D. (1985) Sequential Analysis: Tests and Confidence Intervals, Spring-Verlag, New York, NY
22. Sparks, R. S. (2000) CUSUM charts for signalling varying location shifts. Journal of Quality Technology, 32, pp. 157-171.
23. Wald, A. (1947) Sequential Analysis, Wiley, New York, NY
24. Wu, Z., Wang, P. H. and Wang, Q. N. (2008) A loss function-based adaptive control chart for monitoring the process mean and variance. The International Journal of Advanced Manufacturing Technology
25. Yashchin, E. (1995) Estimating the current mean of a process subject to abrupt changes. Technometrics, 37, pp. 311-323.
26. Zhao, Y., Tsung, F. and Wang, Z. (2005) Dual CUSUM control schemes for detecting a range of mean shifts. IIE Transactions, 37, pp. 1047-1057.