Optimal discrimination designs for exponential regression models

Biedermann, S., Dette, H. and Pepelyshev, Andrey ORCID: https://orcid.org/0000-0001-5634-5559 2007. Optimal discrimination designs for exponential regression models. Journal of Statistical Planning and Inference 137 (8) , pp. 2579-2592. 10.1016/j.jspi.2006.03.015

Full text not available from this repository.

Abstract

We investigate optimal designs for discriminating between exponential regression models of different complexity, which are widely used in the biological sciences; see, e.g., Landaw [1995. Robust sampling designs for compartmental models under large prior eigenvalue uncertainties. Math. Comput. Biomed. Appl. 181–187] or Gibaldi and Perrier [1982. Pharmacokinetics. Marcel Dekker, New York]. We discuss different approaches for the construction of appropriate optimality criteria, and find sharper upper bounds on the number of support points of locally optimal discrimination designs than those given by Caratheodory's Theorem. These results greatly facilitate the numerical construction of optimal designs. Various examples of optimal designs are then presented and compared to different other designs. Moreover, to protect the experiment against misspecifications of the nonlinear model parameters, we adapt the design criteria such that the resulting designs are robust with respect to such misspecifications and, again, provide several examples, which demonstrate the advantages of our approach.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Uncontrolled Keywords:	Compartmental model; Model discrimination; Discrimination design; Locally optimal design; Robust optimal design; Maximin optimal design
Publisher:	Elsevier
ISSN:	0378-3758
Last Modified:	24 Oct 2022 11:42
URI:	https://orca.cardiff.ac.uk/id/eprint/49053

Citation Data

Cited 4 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item