In a previous paper, it was shown that parameter-effects nonlinearities of a nonlinear regression model-experimental design-parameterization combination can be quantified by means of a parameter-effects curvature array $A$ based on second derivatives of the model function. In this paper, the individual terms of $A$ are interpreted and local compensation methods are suggested. A method of computing the parameter-effects array corresponding to a transformed set of parameters is given and we discuss how this result could be used to determine reparameterizations which have zero local parameter-effects nonlinearity.
@article{1176345633,
author = {Bates, Douglas M. and Watts, Donald G.},
title = {Parameter Transformations for Improved Approximate Confidence Regions in Nonlinear Least Squares},
journal = {Ann. Statist.},
volume = {9},
number = {1},
year = {1981},
pages = { 1152-1167},
language = {en},
url = {http://dml.mathdoc.fr/item/1176345633}
}
Bates, Douglas M.; Watts, Donald G. Parameter Transformations for Improved Approximate Confidence Regions in Nonlinear Least Squares. Ann. Statist., Tome 9 (1981) no. 1, pp. 1152-1167. http://gdmltest.u-ga.fr/item/1176345633/