For the Fourier regression model, we determine optimal designs for
identifying the order of periodicity. It is shown that the optimal design
problem for trigonometric regression models is equivalent to the problem of
optimal design for discriminating between certain homo-and heteroscedastic
polynomial regression models. These optimization problems are then solved using
the theory of canonical moments, and the optimal discriminating designs for the
Fourier regression model can be found explicitly. In contrast to many other
optimality criteria for the trigonometric regression model, the optimal
discriminating designs are not uniformly distributed on equidistant points.
Publié le : 1998-08-14
Classification:
Fourier regression model,
optimal design,
model discrimination,
heteroscedastic polynomial regression,
canonical moments,
62K05,
62J05
@article{1024691251,
author = {Dette, Holger and Haller, Gerd},
title = {Optimal designs for the identification of the order of a Fourier
regression},
journal = {Ann. Statist.},
volume = {26},
number = {3},
year = {1998},
pages = { 1496-1521},
language = {en},
url = {http://dml.mathdoc.fr/item/1024691251}
}
Dette, Holger; Haller, Gerd. Optimal designs for the identification of the order of a Fourier
regression. Ann. Statist., Tome 26 (1998) no. 3, pp. 1496-1521. http://gdmltest.u-ga.fr/item/1024691251/