To assess the adequacy of a nonreplicated linear regression model,
Christensen introduced the concepts of orthogonal between- and within-cluster
lack of fit with corresponding optimal tests. However, the properties of these
tests depend on the choice of near-replicate clusters. In this paper, a graph
theoretic framework is presented to represent candidate clusterings. A
clustering is then selected according to a proposed maximin power criterion
from among the clusterings consistent with a specified graph on the predictor
settings. Examples are given to illustrate the methodology.
Publié le : 1998-08-14
Classification:
Regression,
lack of fit,
nonreplication,
between clusters,
within clusters,
maximin power,
graph theory,
62J05,
62F03
@article{1024691249,
author = {Miller, Forrest R. and Neill, James W. and Sherfey, Brian W.},
title = {Maximin clusters for near-replicate regression lack of fit
tests},
journal = {Ann. Statist.},
volume = {26},
number = {3},
year = {1998},
pages = { 1411-1433},
language = {en},
url = {http://dml.mathdoc.fr/item/1024691249}
}
Miller, Forrest R.; Neill, James W.; Sherfey, Brian W. Maximin clusters for near-replicate regression lack of fit
tests. Ann. Statist., Tome 26 (1998) no. 3, pp. 1411-1433. http://gdmltest.u-ga.fr/item/1024691249/