Abstract
A statistical procedure is robust if its behavior is not very sensitive to the assumptions which justify it. In classical statistics these are assumptions about a probability model {P θ ,θ∈Θ} for the observations in Y, and about a loss function L connecting the decision and unknown parameter value. In Bayesian statistics, there is in addition an assumed prior distribution.
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References
Box, G. E. P. and Tiao, G. C. (1973), Bayesian Inference in Statistical Analysis. Reading: Addison-Wesley.
Doob, J. L. (1949), Applications of the theory of martingales, Colloques Internationaux de Centre National de la Recherche Scientific Paris 22–28.
DeRobertis, L. and J. A. Hartigan (1981), Bayesian inference using intervals of measures, The Annals of Statistics 9, 235–244.
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© 1983 Springer-Verlag New York Inc.
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Hartigan, J.A. (1983). Robustness of Bayes Methods. In: Bayes Theory. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-8242-3_12
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DOI: https://doi.org/10.1007/978-1-4613-8242-3_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4613-8244-7
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