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Bootstrapping the sample means for stationary mixing sequences


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  • Shao, Qi-Man
  • Yu, Hao
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    We propose a circular block resampling procedure to modify Künsch's moving block bootstrap. Our procedure has the special feature that the resampled data are like drawing from the empirical distribution function of dependent observations. No information is lost concerning the nature of dependency of the original observations coming from a general stationary sequence. We prove two general theorems on bootstrapping sample means for stationary sequences. Applications to stationary [alpha]-mixing, [rho]-mixing and [phi]-mixing sequences are also discussed.

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    Bibliographic Info

    Article provided by Elsevier in its journal Stochastic Processes and their Applications.

    Volume (Year): 48 (1993)
    Issue (Month): 1 (October)
    Pages: 175-190

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    Handle: RePEc:eee:spapps:v:48:y:1993:i:1:p:175-190

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    Keywords: bootstrap sample mean mixing sequences;


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    Cited by:
    1. Sharipov, Olimjon Sh. & Wendler, Martin, 2013. "Normal limits, nonnormal limits, and the bootstrap for quantiles of dependent data," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 1028-1035.
    2. Psaradakis, Zacharias, 2001. "On bootstrap inference in cointegrating regressions," Economics Letters, Elsevier, vol. 72(1), pages 1-10, July.
    3. Dehling, Herold & Fried, Roland & Sharipov, Olimjon Sh. & Vogel, Daniel & Wornowizki, Max, 2013. "Estimation of the variance of partial sums of dependent processes," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 141-147.
    4. Andrés Alonso & Daniel Peña & Juan Romo, 2003. "Resampling time series using missing values techniques," Annals of the Institute of Statistical Mathematics, Springer, vol. 55(4), pages 765-796, December.
    5. Hwang, Eunju & Shin, Dong Wan, 2012. "Strong consistency of the stationary bootstrap under ψ-weak dependence," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 488-495.
    6. Csörgo, Miklós & Yu, Hao, 1997. "Estimation of total time on test transforms for stationary observations," Stochastic Processes and their Applications, Elsevier, Elsevier, vol. 68(2), pages 229-253, June.


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