In this paper efficient designs are determined when Anderson's procedure is applied in order to identify the degree of a multivariate polynomial regression model. It is shown that the optimal designs are very closely related to model robust designs which maximize a weighted p-mean of D-efficiencies. As a consequence we obtain designs with high efficiency for model discrimination and for the statistical analysis in the
identified model.
Publié le : 1997-06-14
Classification:
Multifactor experiments,
model discrimination,
optimal designs,
invariance,
62K05,
62G10
@article{1069362742,
author = {Dette, Holger and R\"oder, Ingo},
title = {Optimal discrimination designs for multifactor experiments},
journal = {Ann. Statist.},
volume = {25},
number = {6},
year = {1997},
pages = { 1161-1175},
language = {en},
url = {http://dml.mathdoc.fr/item/1069362742}
}
Dette, Holger; Röder, Ingo. Optimal discrimination designs for multifactor experiments. Ann. Statist., Tome 25 (1997) no. 6, pp. 1161-1175. http://gdmltest.u-ga.fr/item/1069362742/