This paper presents in a simple and unified framework the Least-Squares approximation of posterior expectations. Particular structures of the sampling process and of the prior distribution are used to organize and to generalize previous results. The two basic structures are obtained by considering unbiased estimators and exchangeable processes. These ideas are applied to the estimation of the mean. Sufficient reduction of the data is analysed when only the Least-Squares approximation is involved.
@article{urn:eudml:doc:40821,
title = {Least squares approximation in Bayesian analysis.},
journal = {Trabajos de Estad\'\i stica e Investigaci\'on Operativa},
volume = {31},
year = {1980},
pages = {207-222},
zbl = {0467.62029},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:40821}
}
Mouchart, Michel; Simar, Léopold. Least squares approximation in Bayesian analysis.. Trabajos de Estadística e Investigación Operativa, Tome 31 (1980) pp. 207-222. http://gdmltest.u-ga.fr/item/urn:eudml:doc:40821/