The University of Southampton
University of Southampton Institutional Repository

A comparison of design and model selection methods for supersaturated experiments

Record type: Article

Various design and model selection methods are available for supersaturated designs having more factors than runs but little research is available on their comparison and evaluation. Simulated experiments are used to evaluate the use of E(s2)-optimal and Bayesian D-optimal designs and to compare three analysis strategies representing regression, shrinkage and a novel model-averaging procedure. Suggestions are made for choosing the values of the tuning constants for each approach. Findings include that (i) the preferred analysis is via shrinkage; (ii) designs with similar numbers of runs and factors can be effective for a considerable number of active effects of only moderate size; and (iii) unbalanced designs can perform well. Some comments are made on the performance of the design and analysis methods when effect sparsity does not hold.

Full text not available from this repository.

Citation

Marley, Christopher J. and Woods, David C. (2010) A comparison of design and model selection methods for supersaturated experiments Computational Statistics & Data Analysis, 54, (12), pp. 3158-3167. (doi:10.1016/j.csda.2010.02.017).

More information

e-pub ahead of print date: 2 March 2010
Published date: 1 December 2010
Keywords: bayesian D-optimal designs, E(s2)-optimal designs, effect sparsity, gauss–dantzig selector, main effects, screening, simulation
Organisations: Southampton Statistical Research Inst.

Identifiers

Local EPrints ID: 151259
URI: http://eprints.soton.ac.uk/id/eprint/151259
ISSN: 0167-9473
PURE UUID: 0cf59c04-b230-46af-a82d-898dfd3b8702

Catalogue record

Date deposited: 10 May 2010 09:23
Last modified: 18 Jul 2017 12:56

Export record

Altmetrics

Contributors

Author: Christopher J. Marley
Author: David C. Woods

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×