Design selection criteria for discrimination between nested models for binomial data
Design selection criteria for discrimination between nested models for binomial data
The aim of an experiment is often to enable discrimination between competing forms for a response model. We consider this problem when there are two competing generalized linear models (GLMs) for a binomial response. These models are assumed to have a common link function with the linear predictor of one model nested within that of the other. We consider selection of a continuous design for use in a non-sequential strategy and investigate a new criterion, TE-optimality, based on the difference in the deviances from the two models. A comparison is made with three existing design selection criteria, namely T-, Ds- and D-optimality. Issues are raised through the study of two examples in which designs are assessed using simulation studies of the power to reject the null hypothesis of the simpler model being correct, when the data are generated from the larger model. Parameter estimation for these designs is also discussed and a simple method is investigated of combining designs to form a hybrid design to achieve both model discrimination and estimation. Such a method may offer a computational advantage over the use of a compound criterion and the similar performance of the resulting designs is illustrated in an example.
Southampton Statistical Sciences Research Institute, University of Southampton
Waterhouse, T. H.
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Woods, D. C.
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Eccleston, J. A.
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Lewis, S. M.
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8 February 2006
Waterhouse, T. H.
903c4eed-4089-4190-ae66-3ebd75c33eca
Woods, D. C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Eccleston, J. A.
f2f29edd-66f2-47e2-9f59-abb30a7bc615
Lewis, S. M.
a69a3245-8c19-41c6-bf46-0b3b02d83cb8
Waterhouse, T. H., Woods, D. C., Eccleston, J. A. and Lewis, S. M.
(2006)
Design selection criteria for discrimination between nested models for binomial data
(S3RI Methodology Working Papers, M06/02)
Southampton, UK.
Southampton Statistical Sciences Research Institute, University of Southampton
20pp.
Record type:
Monograph
(Working Paper)
Abstract
The aim of an experiment is often to enable discrimination between competing forms for a response model. We consider this problem when there are two competing generalized linear models (GLMs) for a binomial response. These models are assumed to have a common link function with the linear predictor of one model nested within that of the other. We consider selection of a continuous design for use in a non-sequential strategy and investigate a new criterion, TE-optimality, based on the difference in the deviances from the two models. A comparison is made with three existing design selection criteria, namely T-, Ds- and D-optimality. Issues are raised through the study of two examples in which designs are assessed using simulation studies of the power to reject the null hypothesis of the simpler model being correct, when the data are generated from the larger model. Parameter estimation for these designs is also discussed and a simple method is investigated of combining designs to form a hybrid design to achieve both model discrimination and estimation. Such a method may offer a computational advantage over the use of a compound criterion and the similar performance of the resulting designs is illustrated in an example.
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glm_discrim.pdf
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Published date: 8 February 2006
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Local EPrints ID: 19343
URI: http://eprints.soton.ac.uk/id/eprint/19343
PURE UUID: 0653a082-c124-48bf-b315-fbf5e9f27afc
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Date deposited: 08 Feb 2006
Last modified: 16 Mar 2024 03:14
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Author:
T. H. Waterhouse
Author:
J. A. Eccleston
Author:
S. M. Lewis
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