Scientific learning is seen as an iterative process employing Criticism and Estimation. Sampling theory use of predictive distributions for model criticism is examined and also the implications for significance tests and the theory of precise measurement. Normal theory examples and ridge estimates are considered. Predictive checking functions for transformation, serial correlation, and bad values are reviewed as is their relation with Bayesian options. Robustness is seen from a Bayesian view point and examples are given. The bad value problem is also considered and comparison with M estimators is made.
@article{urn:eudml:doc:40834,
title = {Sampling inference, Bayes' inference and robustness in the advancement of learning.},
journal = {Trabajos de Estad\'\i stica e Investigaci\'on Operativa},
volume = {31},
year = {1980},
pages = {366-370},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:40834}
}
Box, George E. P. Sampling inference, Bayes' inference and robustness in the advancement of learning.. Trabajos de Estadística e Investigación Operativa, Tome 31 (1980) pp. 366-370. http://gdmltest.u-ga.fr/item/urn:eudml:doc:40834/