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Sampling and Bayes' Inference in Scientific Modelling and Robustness

George E. P. Box
Journal of the Royal Statistical Society. Series A (General)
Vol. 143, No. 4 (1980), pp. 383-430
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2982063
Stable URL: http://www.jstor.org/stable/2982063
Page Count: 48
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Sampling and Bayes' Inference in Scientific Modelling and Robustness
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Abstract

Scientific learning is an iterative process employing Criticism and Estimation. Correspondingly the formulated model factors into two complementary parts--a predictive part allowing model criticism, and a Bayes posterior part allowing estimation. Implications for significance tests, the theory of precise measurement and for ridge estimates are considered. Predictive checking functions for transformation, serial correlation, bad values, and their relation with Bayesian options are considered. Robustness is seen from a Bayesian viewpoint and examples are given. For the bad value problem a comparison with M estimators is made.

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