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 generalized 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 using examples. An updating formula for D-optimality under a GLM is presented, which improves the computational efficiency of the search.
generalized linear models, optimal design, prior information, screening experiments, simulation
29-41
Woods, D.C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
January 2010
Woods, D.C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Woods, D.C.
(2010)
Robust designs for binary data: applications of simulated annealing.
Journal of Statistical Computation and Simulation, 80 (1), .
(doi:10.1080/00949650802445367).
Abstract
When the aim of an experiment is the estimation of a generalized 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 using examples. An updating formula for D-optimality under a GLM is presented, which improves the computational efficiency of the search.
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Published date: January 2010
Keywords:
generalized linear models, optimal design, prior information, screening experiments, simulation
Identifiers
Local EPrints ID: 151261
URI: http://eprints.soton.ac.uk/id/eprint/151261
ISSN: 0094-9655
PURE UUID: f0fa240d-ac64-493d-9e9f-b9f57b7de88f
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Date deposited: 10 May 2010 09:52
Last modified: 03 Dec 2019 01:54
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