Hierarchical and Empirical Bayes Extensions

  • Christian P. Robert
Part of the Springer Texts in Statistics book series (STS)


In the previous chapters, we have noticed the ambivalent aspect of Bayesian analysis: It is sufficiently reducing to produce an effective decision, but this efficiency can also be misused. For instance, the subjective aspects of Bayesian analysis can always be modified so that it produces conclusions fixed in advance. Of course, the same misappropriation is possible in a frequentist framework through the choice of the loss or of the estimation criterion, while the classical approach does not distinguish between the subjective and the objective inputs of the analysis. The main point is that, as mentioned in Chapter 3, the choice of a prior distribution should always be justifiable by the statistician, i.e., it must be based on sound (or “repeatable”) arguments. So the fact that Bayesian tools may lead to dishonest inferences cannot be taken as a flaw of the Bayesian paradigm.


Prior Distribution Marginal Distribution Quadratic Loss Minimax Estimator Bayesian Paradigm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 1994

Authors and Affiliations

  • Christian P. Robert
    • 1
  1. 1.URA CNRS 1378 — Dépt. de Math.Université de RouenMont Saint Aignan CedexFrance

Personalised recommendations