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Bootstrapping the empirical distribution of a linear process

Author

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  • El Ktaibi, Farid
  • Gail Ivanoff, B.
  • Weber, Neville C.

Abstract

The validity of the moving block bootstrap for the empirical distribution of a short memory causal linear process is established under simple conditions that do not involve mixing or association. Sufficient conditions can be expressed in terms of the existence of moments of the innovations and summability of the coefficients of the linear model. Applications to one and two sample tests are discussed.

Suggested Citation

  • El Ktaibi, Farid & Gail Ivanoff, B. & Weber, Neville C., 2014. "Bootstrapping the empirical distribution of a linear process," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 134-142.
    • Handle: RePEc:eee:stapro:v:93:y:2014:i:c:p:134-142
    DOI: 10.1016/j.spl.2014.06.019
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    References listed on IDEAS

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    1. Dragan Radulović, 2012. "Necessary and sufficient conditions for the moving blocks bootstrap central limit theorem of the mean," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(2), pages 343-357.
    2. Dragan Radulović, 2009. "Another look at the disjoint blocks bootstrap," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 195-212, May.
    3. Pham, Tuan D. & Tran, Lanh T., 1985. "Some mixing properties of time series models," Stochastic Processes and their Applications, Elsevier, vol. 19(2), pages 297-303, April.
    4. 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.
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