Sampling and Bayes' Inference in Scientific Modelling and Robustness
作者： George E. P. Box
DOI: 10.2307/2982063
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摘要: Abstract : Scientific learning is an iterative process employing Criticism and Estimation. Correspondingly the formulated model factors into two complimentary parts - a predictive part allowing criticism, Bayes posterior estimation. Implications for significance tests, theory of precise measurement, ridge estimates are considered. Predictive checking functions transformation, serial correlation, bad values, their relation with Bayesian options Robustness seen from viewpoint examples given. For value problem comparison M estimators made. (Author)
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