Robust Designs For Binary Data: Applications Of Simulated Annealing
Robust Designs For Binary Data: Applications Of Simulated Annealing
When the aim of an experiment is the estimation of a Generalised Linear Model (GLM), standard designs
from linear model theory may prove inadequate. This paper describes a flexible approach for finding
designs for experiments to estimate GLMs through the use of D-optimality and a simulated annealing
algorithm. A variety of uncertainties in the model can be incorporated into the design search, including
the form of the linear predictor, through use of a robust design selection criterion and a postulated
model space. New methods appropriate for screening experiments and the incorporation of correlations
between possible model parameters are described through examples. An updating formula for Doptimality
under a GLM is presented which improves the computational efficiency of the search.
Southampton Statistical Sciences Research Institute, University of Southampton
Woods, D. C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
9 May 2008
Woods, D. C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Woods, D. C.
(2008)
Robust Designs For Binary Data: Applications Of Simulated Annealing
(S3RI Methodology Working Papers, M08/03)
Southampton, UK.
Southampton Statistical Sciences Research Institute, University of Southampton
15pp.
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Monograph
(Working Paper)
Abstract
When the aim of an experiment is the estimation of a Generalised Linear Model (GLM), standard designs
from linear model theory may prove inadequate. This paper describes a flexible approach for finding
designs for experiments to estimate GLMs through the use of D-optimality and a simulated annealing
algorithm. A variety of uncertainties in the model can be incorporated into the design search, including
the form of the linear predictor, through use of a robust design selection criterion and a postulated
model space. New methods appropriate for screening experiments and the incorporation of correlations
between possible model parameters are described through examples. An updating formula for Doptimality
under a GLM is presented which improves the computational efficiency of the search.
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51200-01.pdf
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Published date: 9 May 2008
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Local EPrints ID: 51200
URI: http://eprints.soton.ac.uk/id/eprint/51200
PURE UUID: 25b18796-4cab-48aa-a7ee-013e36eed8e6
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Date deposited: 09 May 2008
Last modified: 16 Mar 2024 03:14
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