Abstract
A statistical model is characterized by a family of probability distribution functions. All inferences are then conditional on the hypothesis formalised by this family.
The statistician often needs to protect himself against the consequences of a gross error relative to the basic hypothesis: either a specification error for the functionnal form of p(x|θ), or the treatment of outliers. It will be shown in this paper that the Bayesian approach offers a natural framework for treating this kind of problem. Different methods are presented: robustness analysis considering the sensitivity of inference to the model specification; and approximations to Bayesian solutions which are for a large class of models and sometimes preferable to the exact solutions valid only for a particular model.
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© 1983 Springer-Verlag New York Inc.
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Simar, L. (1983). Protecting Against Gross Errors: The Aid of Bayesian Methods. In: Florens, J.P., Mouchart, M., Raoult, J.P., Simar, L., Smith, A.F.M. (eds) Specifying Statistical Models. Lecture Notes in Statistics, vol 16. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-5503-1_1
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DOI: https://doi.org/10.1007/978-1-4612-5503-1_1
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