Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Optimal designs for discriminating between dose-response models in toxicology studies

Dette, Holger, Pepelyshev, Andrey ORCID:, Shpilev, Piter and Wong, Weng Kee 2010. Optimal designs for discriminating between dose-response models in toxicology studies. Bernoulli 16 (4) , pp. 1164-1176. 10.3150/10-BEJ257

[thumbnail of Dette 2010.pdf]
PDF - Published Version
Download (158kB) | Preview


We consider design issues for toxicology studies when we have a continuous response and the true mean response is only known to be a member of a class of nested models. This class of non-linear models was proposed by toxicologists who were concerned only with estimation problems. We develop robust and efficient designs for model discrimination and for estimating parameters in the selected model at the same time. In particular, we propose designs that maximize the minimum of D- or D1-efficiencies over all models in the given class. We show that our optimal designs are efficient for determining an appropriate model from the postulated class, quite efficient for estimating model parameters in the identified model and also robust with respect to model misspecification. To facilitate the use of optimal design ideas in practice, we have also constructed a website that freely enables practitioners to generate a variety of optimal designs for a range of models and also enables them to evaluate the efficiency of any design.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Uncontrolled Keywords: continuous design; locally optimal design; maximin optimal design; model discrimination; robust design
Additional Information: Pdf uploaded in accordance with publisher's policy at (accessed 25/02/2014)
Publisher: Bernoulli Society for Mathematical Statistics and Probability
ISSN: 1350-7265
Date of First Compliant Deposit: 30 March 2016
Last Modified: 14 May 2023 02:03

Citation Data

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

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics