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Abrupt change in mean using block bootstrap and avoiding variance estimation

Author

Listed:
  • Barbora Peštová

    (The Czech Academy of Sciences)

  • Michal Pešta

    (Charles University)

Abstract

We deal with sequences of weakly dependent observations that are naturally ordered in time. Their constant mean is possibly subject to change at most once at some unknown time point. The aim is to test whether such an unknown change has occurred or not. The change point methods presented here rely on ratio type test statistics based on maxima of the cumulative sums. These detection procedures for the abrupt change in mean are also robustified by considering a general score function. The main advantage of the proposed approach is that the variance of the observations neither has to be known nor estimated. The asymptotic distribution of the test statistic under the no change null hypothesis is derived. Moreover, we prove the consistency of the test under the alternatives. A block bootstrap method is developed in order to obtain better approximations for the test’s critical values. The validity of the bootstrap algorithm is shown. The results are illustrated through a simulation study, which demonstrates computational efficiency of the procedures. A practical application to real data is presented as well.

Suggested Citation

  • Barbora Peštová & Michal Pešta, 2018. "Abrupt change in mean using block bootstrap and avoiding variance estimation," Computational Statistics, Springer, vol. 33(1), pages 413-441, March.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:1:d:10.1007_s00180-017-0785-4
    DOI: 10.1007/s00180-017-0785-4
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    References listed on IDEAS

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