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A comparison of design and model selection methods for supersaturated experiments

A comparison of design and model selection methods for supersaturated experiments
A comparison of design and model selection methods for supersaturated experiments
Various design and model selection methods are available for supersatu-
rated designs having more factors than runs but little research is available on
their comparison and evaluation. In this paper, 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
M09/20
Southampton Statistical Sciences Research Institute, University of Southampton
Marley, Christopher J.
cf0ba89e-7868-4df0-9bb4-31004497e2ae
Woods, David C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Marley, Christopher J.
cf0ba89e-7868-4df0-9bb4-31004497e2ae
Woods, David C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c

Marley, Christopher J. and Woods, David C. (2009) A comparison of design and model selection methods for supersaturated experiments (S3RI Methodology Working Papers, M09/20) Southampton, UK. Southampton Statistical Sciences Research Institute, University of Southampton 22pp. (Submitted)

Record type: Monograph (Working Paper)

Abstract

Various design and model selection methods are available for supersatu-
rated designs having more factors than runs but little research is available on
their comparison and evaluation. In this paper, 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

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More information

Submitted date: 4 November 2009
Organisations: Southampton Statistical Research Inst.

Identifiers

Local EPrints ID: 69458
URI: http://eprints.soton.ac.uk/id/eprint/69458
PURE UUID: 47aa2d84-9406-496e-b6bb-5b44950c1809
ORCID for David C. Woods: ORCID iD orcid.org/0000-0001-7648-429X

Catalogue record

Date deposited: 17 Nov 2009
Last modified: 14 Mar 2024 02:44

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Contributors

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

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