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Detecting abrupt changes in a piecewise locally stationary time series

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  • Last, Michael
  • Shumway, Robert
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    Abstract

    Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback-Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 99 (2008)
    Issue (Month): 2 (February)
    Pages: 191-214

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    Handle: RePEc:eee:jmvana:v:99:y:2008:i:2:p:191-214

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    Related research

    Keywords: Change-point Locally stationary Frequency domain Kullback-Leibler;

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    Cited by:
    1. Jin, Hao & Zhang, Jinsuo, 2010. "Subsampling tests for variance changes in the presence of autoregressive parameter shifts," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2255-2265, November.

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