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An urn-based Bayesian block bootstrap

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Abstract

Block bootstrap has been introduced in the literature for resampling dependent data, i.e. stationary processes. One of the main assumptions in block bootstrapping is that the blocks of observations are exchangeable, i.e. their joint distribution is immune to permutations. In this paper we propose a new Bayesian approach to block bootstrapping, starting from the construction of exchangeable blocks. Our sampling mechanism is based on a particular class of reinforced urn processes.

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Authors and Affiliations

  1. Department of Statistics (IMSV), University of Bern, Sidlerstrasse 5, 3012, Bern, Switzerland

    Pasquale Cirillo

  2. Department of Decision Sciences, Bocconi University, Milan, Italy

    Pietro Muliere

Authors
  1. Pasquale Cirillo
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  2. Pietro Muliere
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Correspondence to Pasquale Cirillo.

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Cirillo, P., Muliere, P. An urn-based Bayesian block bootstrap. Metrika 76, 93–106 (2013). https://doi.org/10.1007/s00184-011-0377-1

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  • DOI: https://doi.org/10.1007/s00184-011-0377-1

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