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Resampling

In: Long-Memory Processes

Author

Listed:
  • Jan Beran

    (University of Konstanz, Dept. of Mathematics and Statistics)

  • Yuanhua Feng

    (University of Paderborn, Faculty of Business Administration and Economics)

  • Sucharita Ghosh

    (Swiss Federal Research Institute WSL)

  • Rafal Kulik

    (University of Ottawa, Dept. of Mathematics and Statistics)

Abstract

Resampling or bootstrap methods refer to techniques where statistical inference is based on a simulated distribution of a statistic T n obtained by resampling from an observed sample X 1,…,X n . Inference of this type is always conditional on the sample. In the most general version, no model assumptions are used except for global conditions such as stationarity, existence of some moments, etc. In the most restricted version, a parametric model is specified and resampling is used only as a simple way of obtaining an approximate distribution of T n . Note that different terms such as ‘bootstrap’, ‘resampling’, ‘subsampling’, etc. are used in the literature for different variations of the same general idea. Since there does not seem to be a unified terminology, we use ‘resampling’ and ‘bootstrap’ as synonyms.

Suggested Citation

  • Jan Beran & Yuanhua Feng & Sucharita Ghosh & Rafal Kulik, 2013. "Resampling," Springer Books, in: Long-Memory Processes, edition 127, chapter 0, pages 771-795, Springer.
    • Handle: RePEc:spr:sprchp:978-3-642-35512-7_10
    DOI: 10.1007/978-3-642-35512-7_10
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