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November, 1981 Parameter Transformations for Improved Approximate Confidence Regions in Nonlinear Least Squares
Douglas M. Bates, Donald G. Watts
Ann. Statist. 9(6): 1152-1167 (November, 1981). DOI: 10.1214/aos/1176345633

Abstract

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.

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Douglas M. Bates. Donald G. Watts. "Parameter Transformations for Improved Approximate Confidence Regions in Nonlinear Least Squares." Ann. Statist. 9 (6) 1152 - 1167, November, 1981. https://doi.org/10.1214/aos/1176345633

Information

Published: November, 1981
First available in Project Euclid: 12 April 2007

zbMATH: 0493.62060
MathSciNet: MR630099
Digital Object Identifier: 10.1214/aos/1176345633

Subjects:
Primary: 62J02
Secondary: 62F25

Rights: Copyright © 1981 Institute of Mathematical Statistics

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Vol.9 • No. 6 • November, 1981
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