A classification model is easiest to analyze when it has a balanced
design. Many of the nice features of balanced designs are retained by
error-orthogonal designs, which were introduced in a recent paper by the
authors. The present paper defines a kind of ‘‘partially
balanced’’ design and shows that this partial balance is
sufficient to ensure the error-orthogonality of a mixed classification model.
Results are provided that make the partial balance condition easy to check. It
is shown that, for a maximal-rank error-orthogonal design, the Type I sum of
squares for a random effect coincides with the Type II sum of squares.
Publié le : 1999-12-14
Classification:
Mixed linear model,
ANOVA,
sums of squares,
orthogonal block structure,
orthogonal design,
62J10,
62K99
@article{1017939245,
author = {Vanleeuwen, Dawn M. and Birkes, David S. and Seely, Justus F.},
title = {Balance and orthogonality in designs for mixed classification
models},
journal = {Ann. Statist.},
volume = {27},
number = {4},
year = {1999},
pages = { 1927-1947},
language = {en},
url = {http://dml.mathdoc.fr/item/1017939245}
}
Vanleeuwen, Dawn M.; Birkes, David S.; Seely, Justus F. Balance and orthogonality in designs for mixed classification
models. Ann. Statist., Tome 27 (1999) no. 4, pp. 1927-1947. http://gdmltest.u-ga.fr/item/1017939245/