Designs for generalized linear models under model uncertainty
Designs for generalized linear models under model uncertainty
Standard factorial designs may sometimes be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding designs for such experiments which uses a criterion that allows for uncertainty in the link function, the linear predictor or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Designs are investigated for practical applications and their advantages are discussed.
Woods, D. C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
15 May 2005
Woods, D. C.
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Woods, D. C.
(2005)
Designs for generalized linear models under model uncertainty.
International Conference on Design of Experiments, Memphis, USA.
12 - 14 May 2005.
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Abstract
Standard factorial designs may sometimes be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding designs for such experiments which uses a criterion that allows for uncertainty in the link function, the linear predictor or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Designs are investigated for practical applications and their advantages are discussed.
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Published date: 15 May 2005
Venue - Dates:
International Conference on Design of Experiments, Memphis, USA, 2005-05-12 - 2005-05-14
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Statistics
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Local EPrints ID: 15868
URI: http://eprints.soton.ac.uk/id/eprint/15868
PURE UUID: 75d3d630-1d9e-4726-93b4-82ce8cf90b98
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Date deposited: 06 Jun 2005
Last modified: 12 Dec 2021 03:11
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