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Robust Sequential Algorithms for the Detection of Changes in Data Generating Processes

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Abstract

We consider the case where data sequences may be generated by either one of a number of non-parametrically defined processes and where the data generating process may change at any point in time. The objective is to effectively track the latter changes, where each acting process is essentially represented by a whole class of parametrically defined processes. We present, analyze and evaluate robust sequential algorithms which attain the objective for a variety of scenarios. Our robust algorithms consist of appropriate modifications of previously presented parametric sequential algorithms, to mainly resist the occurrence of occasional data outliers in terms of dramatic performance deterioration.

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References

  1. Page, E.S.: Continuous inspection schemes. Biometrika 41, 100–115 (1954)

    MathSciNet  MATH  Google Scholar 

  2. Lorden, G.: Procedures for reacting to a change in distribution. Ann. Math Stat. 42, 1897–1908 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bansal, R.K., Papantoni-Kazakos, P.: An algorithm for detecting a change in a stochastic process. IEEE Trans. Inf. Theory IT-32, 227–235 (1986)

    Article  MathSciNet  Google Scholar 

  4. Bansal, R.K., Papantoni-Kazakos, P.: Outlier resistant algorithms for detecting a change in stochastic process. IEEE Trans. Inf. Theory IT-35, 521–535 (1989)

    Article  MathSciNet  Google Scholar 

  5. Burrell, A.T.: Traffic management in broadband integrated services digital networks. Ph.D. dissertation, University of Virginia (1994)

  6. Burrell, A.T., Makrakis, D., Papantoni-Kazakos, P.: Traffic monitoring for capacity allocation of multimedia traffic in ATM broadband networks. Telecommun. Syst. 9, 173–206 (1998)

    Article  Google Scholar 

  7. Burrell, A.T., Papantoni-Kazakos, P.: Extended sequential algorithms for detecting changes in acting stochastic processes. IEEE Trans. Syst. Man Cybern. 28(5), 703–710 (1998)

    Article  Google Scholar 

  8. Girshick, M.A., Rubin, H.: A Bayes approach to a quality control model. Ann. Math Stat. 23, 114–125 (1952)

    Article  MathSciNet  MATH  Google Scholar 

  9. Moustakidis, G.V.: Optimal stopping times for detecting changes in distributions. Ann. Stat. 14(4), 1379–1387 (1986)

    Article  Google Scholar 

  10. Papantoni-Kazakos, P.: Some aspects of qualitative robustness in time series. In: Robust and Nonlinear Time Series Analysis. Lecture Notes in Stats., vol. 26, pp. 218–246. Springer, New York (1985)

    Google Scholar 

  11. Pollak, M.: Optimal detection of a change in distribution. Ann. Stat. 13(1), 206–227 (1985)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering Department, University of Colorado at Denver, Box 110 P.O. Box 173364, Denver, CO, 80217, USA

    P. Papantoni-Kazakos

  2. Computer Science Department, Oklahoma State University, 219 Math Science, Stillwater, OK, 74078, USA

    Anthony Burrell

Authors
  1. P. Papantoni-Kazakos
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  2. Anthony Burrell
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Correspondence to P. Papantoni-Kazakos.

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Papantoni-Kazakos, P., Burrell, A. Robust Sequential Algorithms for the Detection of Changes in Data Generating Processes. J Intell Robot Syst 60, 3–17 (2010). https://doi.org/10.1007/s10846-010-9405-z

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  • DOI: https://doi.org/10.1007/s10846-010-9405-z

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