MARGINAL CURVATURES FOR FUNCTIONS OF PARAMETERS IN NONLINEAR REGRESSION

The marginal curvature by Clarke (1987) for individual parameters in nonlinear models not only improves the inference on each parameter but also has been found useful in experimental design for nonlinear models. In this article we develop the marginal curvature for functions of parameters. We show t...

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Bibliographic Details
Published in:Statistica Sinica 1998-04, Vol.8 (2), p.467-476
Main Authors: Kang, Gunseog, Rawlings, John O.
Format: Article
Language:English
Subjects:
Curvature
Experiment design
Inference
Linear approximation
Mathematical independent variables
Mathematical transformations
Nonlinearity
Parametric models
Point estimators
Regression analysis
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Summary:The marginal curvature by Clarke (1987) for individual parameters in nonlinear models not only improves the inference on each parameter but also has been found useful in experimental design for nonlinear models. In this article we develop the marginal curvature for functions of parameters. We show that, for a given reparametrization, the marginal curvatures for the transformed parameters can be computed without determining the inverse transformation. Furthermore, the marginal curvature for a function of parameters depends only on the marginal curvatures of the original parameters and on the derivatives of the function with respect to the parameters involved in that function. We also present a more efficient computing algorithm of Clarke's marginal curvature measure. The resulting expression enables us to compare Clarke's measure with other available measures.
ISSN:1017-0405
1996-8507