The problem of testing the correctness of a nonlinear response function against unspecified general alternatives is considered. The proposed test statistic is a modification of a nonlinear analogue to the well-known linear regression lack-of-fit test and can be used with or without replication. Asymptotically valid critical points can be obtained from a central $F$-distribution. Also, when the null model is the orthogonal projection of the true model, the test statistic is asymptotically comparable to a random variable with a noncentral $F$-distribution.
Publié le : 1988-06-14
Classification:
Regression,
nonlinear,
model adequacy,
nonreplication,
62J02,
62F03
@article{1176350831,
author = {Neill, James W.},
title = {Testing for Lack of Fit in Nonlinear Regression},
journal = {Ann. Statist.},
volume = {16},
number = {1},
year = {1988},
pages = { 733-740},
language = {en},
url = {http://dml.mathdoc.fr/item/1176350831}
}
Neill, James W. Testing for Lack of Fit in Nonlinear Regression. Ann. Statist., Tome 16 (1988) no. 1, pp. 733-740. http://gdmltest.u-ga.fr/item/1176350831/