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Bayesian Inference Under Partial Prior Information

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  • Elias Moreno
  • Francesco Bertolino
  • Walter Racugno

Abstract

Partial prior information on the marginal distribution of an observable random variable is considered. When this information is incorporated into the statistical analysis of an assumed parametric model, the posterior inference is typically non‐robust so that no inferential conclusion is obtained. To overcome this difficulty a method based on the standard default prior associated to the model and an intrinsic procedure is proposed. Posterior robustness of the resulting inferences is analysed and some illustrative examples are provided.

Suggested Citation

  • Elias Moreno & Francesco Bertolino & Walter Racugno, 2003. "Bayesian Inference Under Partial Prior Information," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(3), pages 565-580, September.
    • Handle: RePEc:bla:scjsta:v:30:y:2003:i:3:p:565-580
    DOI: 10.1111/1467-9469.00349
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    Cited by:

    1. Emilio Déniz & José Sarabia & F. Vázquez Polo, 2009. "Robust Bayesian bonus-malus premiums under the conditional specification model," Statistical Papers, Springer, vol. 50(3), pages 465-480, June.

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