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Designs for Generalized Linear Models with Several Variables and Model Uncertainty

Woods, D. C., Lewis, S. M., Eccleston, J. A. and Russell, K. G. (2006) Designs for Generalized Linear Models with Several Variables and Model Uncertainty Technometrics

Record type: Article

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 exact 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. Exact designs are investigated for two applications and their advantages over factorial and central composite designs are demonstrated.

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e-pub ahead of print date: 8 February 2006
Organisations: Southampton Statistical Research Inst.

Identifiers

Local EPrints ID: 19342
URI: http://eprints.soton.ac.uk/id/eprint/19342
ISSN: 0040-1706
PURE UUID: 4e48c352-c9f9-4a0f-b768-1fe7aac85033

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Date deposited: 08 Feb 2006
Last modified: 17 Jul 2017 16:31

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Contributors

Author: D. C. Woods
Author: S. M. Lewis
Author: J. A. Eccleston
Author: K. G. Russell

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