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Designs for generalized linear models with several variables and model uncertainty

By D. C. Woods, S. M. Lewis, J. A. Eccleston and K. G. Russell


Standard factorial designs may sometimes be inadequate for<br/>experiments that aim to estimate a generalized linear model, for<br/>example, for describing a binary response in terms of several<br/>variables. A method is proposed for finding exact designs for such<br/>experiments which uses a criterion that allows for uncertainty in<br/>the link function, the linear predictor or the model parameters,<br/>together with a design search. Designs are assessed and compared<br/>by simulation of the distribution of efficiencies relative to<br/>locally optimal designs over a space of possible models. Exact<br/>designs are investigated for two applications and their advantages<br/>over factorial and central composite designs are demonstrated

Topics: QA, HA
Year: 2006
OAI identifier: oai:eprints.soton.ac.uk:15828
Provided by: e-Prints Soton

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