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
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
Text
s3ri-workingpaper-M09-20.pdf
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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
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Date deposited: 17 Nov 2009
Last modified: 14 Mar 2024 02:44
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Contributors
Author:
Christopher J. Marley
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