‘Nearly’ universally optimal designs for models with correlated observations

Dette, Holger, Pepelyshev, Andrey ORCID: https://orcid.org/0000-0001-5634-5559 and Zhigljavsky, Anatoly ORCID: https://orcid.org/0000-0003-0630-8279 2014. ‘Nearly’ universally optimal designs for models with correlated observations. Computational Statistics & Data Analysis 71 , pp. 1103-1112. 10.1016/j.csda.2013.02.002

Full text not available from this repository.

Abstract

The problem of determining optimal designs for least squares estimation is considered in the common linear regression model with correlated observations. The approach is based on the determination of ‘nearly’ universally optimal designs, even in the case where the universally optimal design does not exist. For this purpose, a new optimality criterion which reflects the distance between a given design and an ideal universally optimal design is introduced. A necessary condition for the optimality of a given design is established. Numerical methods for constructing these designs are proposed and applied for the determination of optimal designs in a number of specific instances. The results indicate that the new ‘nearly’ universally optimal designs have good efficiencies with respect to common optimality criteria.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Uncontrolled Keywords:	Optimal design; Correlated observations; Universally optimal design; Multiplicative algorithms
Publisher:	Elsevier
ISSN:	0167-9473
Date of Acceptance:	1 February 2013
Last Modified:	31 Oct 2022 11:06
URI:	https://orca.cardiff.ac.uk/id/eprint/87462

Citation Data

Cited 2 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item