An explicit formula is derived to compute the $A$-optimal design weights on linearly independent regression vectors, for the mean parameters in a linear model with homoscedastic variances. The formula emerges as a special case of a general result which holds for a wide class of optimality criteria. There are close links to iterative algorithms for computing optimal weights.
Publié le : 1991-09-14
Classification:
General equivalence theorem,
information functions,
matrix means,
algorithms for optimal designs,
$A$-optimality,
$D$-optimality,
$c$-optimality,
62K05
@article{1176348265,
author = {Pukelsheim, Friedrich and Torsney, Ben},
title = {Optimal Weights for Experimental Designs on Linearly Independent Support Points},
journal = {Ann. Statist.},
volume = {19},
number = {1},
year = {1991},
pages = { 1614-1625},
language = {en},
url = {http://dml.mathdoc.fr/item/1176348265}
}
Pukelsheim, Friedrich; Torsney, Ben. Optimal Weights for Experimental Designs on Linearly Independent Support Points. Ann. Statist., Tome 19 (1991) no. 1, pp. 1614-1625. http://gdmltest.u-ga.fr/item/1176348265/