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Nonparametric multiple change-point estimators

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  • Lee, Chung-Bow

Abstract

A simple method is proposed to detect the number of change points in a sequence of independent random variables with no distributional assumption. The method is based on the weighted empirical measures over a window of observations and then runs the window over the full extent of the data. We find that the class of estimators based on our method will be consistent a.s. (almost surely) to the true number of change points and the difference between the true location of change points and the estimated location will be of order O(log n) a.s. Three examples are investigated by the proposed method.

Suggested Citation

  • Lee, Chung-Bow, 1996. "Nonparametric multiple change-point estimators," Statistics & Probability Letters, Elsevier, vol. 27(4), pages 295-304, May.
    • Handle: RePEc:eee:stapro:v:27:y:1996:i:4:p:295-304
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    References listed on IDEAS

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    1. Yao, Yi-Ching, 1988. "Estimating the number of change-points via Schwarz' criterion," Statistics & Probability Letters, Elsevier, vol. 6(3), pages 181-189, February.
    2. Lee, Chung-Bow, 1995. "Estimating the number of change points in a sequence of independent normal random variables," Statistics & Probability Letters, Elsevier, vol. 25(3), pages 241-248, November.
    3. Ferger, Dietmar & Stute, Winfried, 1992. "Convergence of changepoint estimators," Stochastic Processes and their Applications, Elsevier, vol. 42(2), pages 345-351, September.
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    Cited by:

    1. Müller, Hans-Georg & Wai, Newton, 2006. "Asymptotic fluctuations of mutagrams," Statistics & Probability Letters, Elsevier, vol. 76(12), pages 1201-1210, July.
    2. Davis, Richard A. & Hancock, Stacey A. & Yao, Yi-Ching, 2016. "On consistency of minimum description length model selection for piecewise autoregressions," Journal of Econometrics, Elsevier, vol. 194(2), pages 360-368.
    3. Pan, Jianmin & Chen, Jiahua, 2006. "Application of modified information criterion to multiple change point problems," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2221-2241, November.

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