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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Robinson, P.B., Ho, T.Y.: Average run lengths of geometric moving average charts by numerical methods. Technometrics 20(1), 85–93 (1978)
Crowder, S.V.: A simple method for studying run-length distributions of exponentially weighted moving average charts. Technometrics 29(4), 401–407 (1987)
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)
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)
Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Application. Information and System Sciences Series. Prentice Hall Inc, Englewood Cliffs (1993)
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)
Bakhache, B., Nikiforov, I.: Reliable detection of faults in measurement systems. Int. J. Adapt. Control Signal Process. 14(7), 683–700 (2000)
Guépié, B.K., Fillatre, L., Nikiforov, I.: Sequential detection of transient changes. Seq. Anal. 31(4), 528–547 (2012)
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)
Wald, A.: Sequential Analysis. Wiley, New York (1947)
Cox, D.R., Miller, H.D.: The Theory of Stochastic Processes. Wiley, New York (1965)
Shepp, L.A.: A first passage problem for the Wiener process. Ann. Math. Stat. 38(6), 1912–1914 (1967)
Kemperman., J.: The general one-dimensional random walk with absorbing barriers with applications to sequential analysis. ’s-Gravenhage: Excelsiors foto-offset, Dissertation, Amsterdam (1950)
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)
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)
Acknowledgments
The author gratefully acknowledges the research and financial support of this work from the Thales Alenia Space, France.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-66836-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66835-2
Online ISBN: 978-3-319-66836-9
eBook Packages: Computer ScienceComputer Science (R0)