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August 2014 Optimum design accounting for the global nonlinear behavior of the model
Andrej Pázman, Luc Pronzato
Ann. Statist. 42(4): 1426-1451 (August 2014). DOI: 10.1214/14-AOS1232


Among the major difficulties that one may encounter when estimating parameters in a nonlinear regression model are the nonuniqueness of the estimator, its instability with respect to small perturbations of the observations and the presence of local optimizers of the estimation criterion.

We show that these estimability issues can be taken into account at the design stage, through the definition of suitable design criteria. Extensions of $E$-, $c$- and $G$-optimality criteria are considered, which when evaluated at a given $\theta^{0}$ (local optimal design), account for the behavior of the model response $\eta(\theta )$ for $\theta $ far from $\theta^{0}$. In particular, they ensure some protection against close-to-overlapping situations where $\|\eta(\theta )-\eta(\theta^{0})\|$ is small for some $\theta $ far from $\theta^{0}$. These extended criteria are concave and necessary and sufficient conditions for optimality (equivalence theorems) can be formulated. They are not differentiable, but when the design space is finite and the set $\Theta$ of admissible $\theta $ is discretized, optimal design forms a linear programming problem which can be solved directly or via relaxation when $\Theta$ is just compact. Several examples are presented.


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Andrej Pázman. Luc Pronzato. "Optimum design accounting for the global nonlinear behavior of the model." Ann. Statist. 42 (4) 1426 - 1451, August 2014.


Published: August 2014
First available in Project Euclid: 25 June 2014

zbMATH: 1302.62174
MathSciNet: MR3226162
Digital Object Identifier: 10.1214/14-AOS1232

Primary: 62K05
Secondary: 62J02

Keywords: curvature , estimability , nonlinear least-squares , optimal design

Rights: Copyright © 2014 Institute of Mathematical Statistics


Vol.42 • No. 4 • August 2014
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