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On rates of convergence for posterior distributions in infinite–dimensional models

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Author Info

  • Antonio Lijoi

    ()

  • Igor Prünster

    ()

  • Stephen G. Walker

    ()

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    Abstract

    This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. Crucially, no sieve or entropy measures are required and so rates do not depend on the rate of convergence of the corresponding sieve maximum likelihood estimator. In particular, we improve on current rates for mixture models.

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    File URL: http://www.icer.it/docs/wp2004/Pruenster24-04.pdf
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    Bibliographic Info

    Paper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number 24-2004.

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    Length: 16 pages
    Date of creation: Oct 2004
    Date of revision:
    Handle: RePEc:icr:wpmath:24-2004

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    Related research

    Keywords: Hellinger consistency; mixture of Dirichlet process; posterior distribution; rates of convergence;

    This paper has been announced in the following NEP Reports:

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