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Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series

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Algorithmic Learning Theory (ALT 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8139))

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Abstract

Given a heterogeneous time-series sample, it is required to find the points in time (called change points) where the probability distribution generating the data has changed. The data is assumed to have been generated by arbitrary, unknown, stationary ergodic distributions. No modelling, independence or mixing assumptions are made. A novel, computationally efficient, nonparametric method is proposed, and is shown to be asymptotically consistent in this general framework; the theoretical results are complemented with experimental evaluations.

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References

  1. Brodsky, B., Darkhovsky, B.: Nonparametric methods in change-point problems. Mathematics and its applications. Kluwer Academic Publishers (1993)

    Google Scholar 

  2. Basseville, M., Nikiforov, I.: Detection of abrupt changes: theory and application. Prentice Hall information and system sciences series. Prentice Hall (1993)

    Google Scholar 

  3. Csörgö, M., Horváth, L.: Limit theorems in change-point analysis. Wiley Chichester (1997)

    Google Scholar 

  4. Carlstein, E., Lele, S.: Nonparametric change-point estimation for data from an ergodic sequence. Teor. Veroyatnost. i Primenen. 38, 910–917 (1993)

    MathSciNet  Google Scholar 

  5. Giraitis, L., Leipus, R., Surgailis, D.: The change-point problem for dependent observations. Journal of Statistical Planning and Inference 53(3) (1996)

    Google Scholar 

  6. Shields, P.: The Ergodic Theory of Discrete Sample Paths. AMS Bookstore (1996)

    Google Scholar 

  7. Ryabko, D.: Discrimination between B-processes is impossible. Journal of Theoretical Probability 23(2), 565–575 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Ryabko, D., Ryabko, B.: Nonparametric statistical inference for ergodic processes. IEEE Transactions on Information Theory 56(3) (2010)

    Google Scholar 

  9. Khaleghi, A., Ryabko, D.: Locating changes in highly-dependent data with unknown number of change points. In: NIPS, Nevada, United States (2012)

    Google Scholar 

  10. Gray, R.: Prob. Random Processes, & Ergodic Properties. Springer (1988)

    Google Scholar 

  11. Ryabko, D.: Clustering processes. In: ICML, Haifa, Israel, pp. 919–926 (2010)

    Google Scholar 

  12. Khaleghi, A., Ryabko, D., Mary, J., Preux, P.: Online clustering of processes. In: AI & Stats, Canary Islands, pp. 601–609 (2012)

    Google Scholar 

  13. Ryabko, D.: Testing composite hypotheses about discrete ergodic processes. Test 21(2), 317–329 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  14. Billingsley, P.: Ergodic theory and information. Wiley, New York (1965)

    MATH  Google Scholar 

  15. Csiszar, I., Shields, P.: Notes on information theory and statistics. In: Foundations and Trends in Communications and Information Theory (2004)

    Google Scholar 

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Khaleghi, A., Ryabko, D. (2013). Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series. In: Jain, S., Munos, R., Stephan, F., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2013. Lecture Notes in Computer Science(), vol 8139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40935-6_27

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  • DOI: https://doi.org/10.1007/978-3-642-40935-6_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40934-9

  • Online ISBN: 978-3-642-40935-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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