We discuss two numerical approaches to linear minimax estimation in linear models under ellipsoidal parameter restrictions. The first attacks the problem directly, by minimizing the maximum risk among the estimators. The second method is based on the duality between minimax and Bayes estimation, and aims at finding a least favorable prior distribution.
@article{bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1003,
author = {Norbert Gaffke and Berthold Heiligers},
title = {Numerical methods for linear minimax estimation},
journal = {Discussiones Mathematicae Probability and Statistics},
volume = {20},
year = {2000},
pages = {51-62},
zbl = {0960.62057},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1003}
}
Norbert Gaffke; Berthold Heiligers. Numerical methods for linear minimax estimation. Discussiones Mathematicae Probability and Statistics, Tome 20 (2000) pp. 51-62. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1003/
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