Designing for Parameter Subsets in Gaussian Nonlinear Regression Models
Volume 3, Issue 2 (2005), pp. 179–197
Pub. online: 4 August 2022
Type: Research Article
Open Access
Published
4 August 2022
4 August 2022
Abstract
Abstract: This article presents and illustrates several important subset design approaches for Gaussian nonlinear regression models and for linear models where interest lies in a nonlinear function of the model parameters. These design strategies are particularly useful in situations where currentlyused subset design procedures fail to provide designs which can be used to fit the model function. Our original design technique is illustrated in conjuction with D-optimality, Bayesian D-optimality and Kiefer’s Φk-optimality, and is extended to yield subset designs which take account of curvature.