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Bootstrap for the sample mean and for -statistics of mixing and near-epoch dependent processes

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  • Olimjon Sharipov
  • Martin Wendler

Abstract

The validity of various bootstrapping methods has been proved for the sample mean of strongly mixing data. But in many applications, there appear nonlinear statistics of processes that are not strongly mixing. We investigate the nonoverlapping block bootstrap sequences which are near-epoch dependent on strong mixing or absolutely regular processes. This includes linear processes and conditional heteroskedastic processes as well as data from chaotic dynamical systems. We establish the strong consistency of the bootstrap distribution estimator not only for the sample mean, but also for U-statistics, which include such examples as Gini's mean difference or the χ2-test statistic.

Suggested Citation

  • Olimjon Sharipov & Martin Wendler, 2012. "Bootstrap for the sample mean and for -statistics of mixing and near-epoch dependent processes," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(2), pages 317-342.
    • Handle: RePEc:taf:gnstxx:v:24:y:2012:i:2:p:317-342
    DOI: 10.1080/10485252.2012.655274
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    References listed on IDEAS

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    1. Hansen, Bruce E., 1991. "GARCH(1, 1) processes are near epoch dependent," Economics Letters, Elsevier, vol. 36(2), pages 181-186, June.
    2. Dehling, H. & Mikosch, T., 1994. "Random Quadratic Forms and the Bootstrap for U-Statistics," Journal of Multivariate Analysis, Elsevier, vol. 51(2), pages 392-413, November.
    3. Dehling, Herold & Wendler, Martin, 2010. "Central limit theorem and the bootstrap for U-statistics of strongly mixing data," Journal of Multivariate Analysis, Elsevier, vol. 101(1), pages 126-137, January.
    4. Shao, Qi-Man & Yu, Hao, 1993. "Bootstrapping the sample means for stationary mixing sequences," Stochastic Processes and their Applications, Elsevier, vol. 48(1), pages 175-190, October.
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    Cited by:

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    5. Junwei Hu & Lihong Wang, 2023. "A weighted U-statistic based change point test for multivariate time series," Statistical Papers, Springer, vol. 64(3), pages 753-778, June.

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