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Exhaustivité, ancillarité et identification en statistique bayesienne

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  • Jean-Pierre Florens
  • Michel Mouchart

Abstract

A Bayesian experiment is defined by a unique probability on the product of the parameter space and the sample space. This joint probability determines a conditional independance relation which is used for a symmetrical analysis of sufficiency and ancillarity on the parameter and the sample. Identification is then considered as a property of minimal sufficiency on the parameter space. These concepts are extended to conditional models and are shown to be suitable for a study of the exogeneity property in a coherent statistical framework.

Suggested Citation

  • Jean-Pierre Florens & Michel Mouchart, 1986. "Exhaustivité, ancillarité et identification en statistique bayesienne," Annals of Economics and Statistics, GENES, issue 4, pages 63-93.
    • Handle: RePEc:adr:anecst:y:1986:i:4:p:63-93
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    File URL: http://www.jstor.org/stable/20075628
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    Cited by:

    1. Jean-Pierre Florens & Anna Simoni, 2021. "Revisiting Identification Concepts in Bayesian Analysis," Annals of Economics and Statistics, GENES, issue 144, pages 1-38.

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