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An urn-based Bayesian block bootstrap

Author

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  • Pasquale Cirillo

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  • Pietro Muliere

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. Copyright Springer-Verlag 2013

Suggested Citation

  • Pasquale Cirillo & Pietro Muliere, 2013. "An urn-based Bayesian block bootstrap," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 93-106, January.
  • Handle: RePEc:spr:metrik:v:76:y:2013:i:1:p:93-106
    DOI: 10.1007/s00184-011-0377-1
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    File URL: http://hdl.handle.net/10.1007/s00184-011-0377-1
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    References listed on IDEAS

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    1. P. Muliere & P. Secchi, 1996. "Bayesian nonparametric predictive inference and bootstrap techniques," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(4), pages 663-673, December.
    2. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    3. Muliere, P. & Secchi, P. & Walker, S. G., 2000. "Urn schemes and reinforced random walks," Stochastic Processes and their Applications, Elsevier, vol. 88(1), pages 59-78, July.
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