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Recent developments in bootstrap methods for dependent data

Author

Listed:
  • Giuseppe Cavaliere
  • Dimitris N. Politis
  • Anders Rahbek
  • Srijan Sengupta
  • Xiaofeng Shao
  • Yingchuan Wang

Abstract

type="main" xml:id="jtsa12109-abs-0001"> We propose a new resampling method, the dependent random weighting, for both time series and random fields. The method is a generalization of the traditional random weighting in that the weights are made to be temporally or spatially dependent and are adaptive to the configuration of the data. Unlike the block-based bootstrap or subsampling methods, the dependent random weighting can be used for irregularly spaced time series and spatial data without any implementational difficulty. Consistency of the distribution approximation is shown for both equally and unequally spaced time series. Simulation studies illustrate the finite sample performance of the dependent random weighting in comparison with the existing counterparts for both one-dimensional and two-dimensional irregularly spaced data.

Suggested Citation

  • Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Srijan Sengupta & Xiaofeng Shao & Yingchuan Wang, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 315-326, May.
    • Handle: RePEc:bla:jtsera:v:36:y:2015:i:3:p:315-326
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    File URL: http://hdl.handle.net/10.1111/jtsa.12109
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    References listed on IDEAS

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    4. Shao, Xiaofeng, 2010. "The Dependent Wild Bootstrap," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 218-235.
    5. Xiaofeng Shao & Dimitris N. Politis, 2013. "Fixed b subsampling and the block bootstrap: improved confidence sets based on p-value calibration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 161-184, January.
    6. S. Lahiri & Kanchan Mukherjee, 2004. "Asymptotic distributions of M-estimators in a spatial regression model under some fixed and stochastic spatial sampling designs," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(2), pages 225-250, June.
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