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Bounding the Risk Probability

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 700))

Abstract

For some safety–critical applications, it is important to calculate the probability that a discrete time autoregressive (AR) process leaves a given interval at least once during a certain period of time. For example, such AR process can be interpreted as a temporally correlated safety indicator and the interval as a target zone of the process. It is assumed that the safety of the system under surveillance is compromised if the above-mentioned probability becomes too important. This problem has been previously studied in the case of known distributions of the innovation process. Let us assume now that the distributions of the innovation and initial state are unknown but some special bounds for the cumulative distribution functions and/or for the probability density functions are available. Numerical methods to calculate the bounds for the above-mentioned probability are considered in the paper.

I. Nikiforov—The author gratefully acknowledges the research and financial support of this work from the Thales Alenia Space, France.

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References

  1. Robinson, P.B., Ho, T.Y.: Average run lengths of geometric moving average charts by numerical methods. Technometrics 20(1), 85–93 (1978)

    Article  MATH  Google Scholar 

  2. Crowder, S.V.: A simple method for studying run-length distributions of exponentially weighted moving average charts. Technometrics 29(4), 401–407 (1987)

    MathSciNet  MATH  Google Scholar 

  3. Novikov, A.A.: On the first exit time of an autoregressive process beyond a level and an application to the “Change-Point” problem. Teor. Veroyatnost. i Primenen. 35(2), 282–292 (1990)

    MathSciNet  MATH  Google Scholar 

  4. Novikov, A.A., Kordzakhia, N.: Martingales and first passage times of AR(1) sequences. Stochast. Int. J. Probab. Stochast. Process. 80(2–3), 197–210 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Application. Information and System Sciences Series. Prentice Hall Inc, Englewood Cliffs (1993)

    Google Scholar 

  6. Tartakovsky, A., Nikiforov, I., Basseville, M.: Sequential Analysis: Hypothesis Testing and Changepoint Detection. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Taylor & Francis, New York (2014)

    MATH  Google Scholar 

  7. Bakhache, B., Nikiforov, I.: Reliable detection of faults in measurement systems. Int. J. Adapt. Control Signal Process. 14(7), 683–700 (2000)

    Article  MATH  Google Scholar 

  8. Guépié, B.K., Fillatre, L., Nikiforov, I.: Sequential detection of transient changes. Seq. Anal. 31(4), 528–547 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  9. Guépié, B.K., Fillatre, L., Nikiforov, I.: Detecting a suddenly arriving dynamic profile of finite duration. IEEE Trans. Inf. Theory 63(5), 3039–3052 (2017)

    MathSciNet  MATH  Google Scholar 

  10. Wald, A.: Sequential Analysis. Wiley, New York (1947)

    MATH  Google Scholar 

  11. Cox, D.R., Miller, H.D.: The Theory of Stochastic Processes. Wiley, New York (1965)

    MATH  Google Scholar 

  12. Shepp, L.A.: A first passage problem for the Wiener process. Ann. Math. Stat. 38(6), 1912–1914 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  13. Kemperman., J.: The general one-dimensional random walk with absorbing barriers with applications to sequential analysis. ’s-Gravenhage: Excelsiors foto-offset, Dissertation, Amsterdam (1950)

    Google Scholar 

  14. Page, E.S.: An improvement to Wald’s approximation for some properties of sequential tests. J. R. Stat. Soc. Ser. B Methodol. 16(1), 136–139 (1954)

    MathSciNet  MATH  Google Scholar 

  15. Rife, J., Walter, T., Blanch, J.: Overbounding SBAS and GBAS error distributions with excess-mass functions. In: Proceedings of the GNSS 2004 International Symposium on GNSS/GPS, Sydney, Australia, 6–8 December (2004)

    Google Scholar 

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Acknowledgments

The author gratefully acknowledges the research and financial support of this work from the Thales Alenia Space, France.

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Correspondence to Igor Nikiforov .

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Nikiforov, I. (2017). Bounding the Risk Probability. In: Vishnevskiy, V., Samouylov, K., Kozyrev, D. (eds) Distributed Computer and Communication Networks. DCCN 2017. Communications in Computer and Information Science, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-319-66836-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-66836-9_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66835-2

  • Online ISBN: 978-3-319-66836-9

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