When the measurement errors may be assumed to be normal and independent from what is measured a subnormal model may be used. We define a linear and generalized linear hypotheses for these models, and derive F-tests for them. These tests are shown to be UMP for linear hypotheses as well as strictly unbiased and strongly consistent for these hypotheses. It is also shown that the F-tests are invariant for regular transformations, possess structural stability and are almost strongly consistent for generalized linear hypothesis. An application to a mixed model studied by Michalskyi and Zmyślony is shown.
@article{bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1019,
author = {Joao Tiago Mexia and Gerberto Carvalho Dias},
title = {F-tests for generalized linear hypotheses in subnormal models},
journal = {Discussiones Mathematicae Probability and Statistics},
volume = {21},
year = {2001},
pages = {49-62},
zbl = {0984.62041},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1019}
}
Joao Tiago Mexia; Gerberto Carvalho Dias. F-tests for generalized linear hypotheses in subnormal models. Discussiones Mathematicae Probability and Statistics, Tome 21 (2001) pp. 49-62. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1019/
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