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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, 48, (2), pp. 284-292. (doi:10.1198/004017005000000571).

Record type: Article


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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Published date: 2006
Keywords: binary response, d-optimality, logistic regression, robust design, simulation
Organisations: Statistics


Local EPrints ID: 15828
ISSN: 0040-1706
PURE UUID: f566d8f0-b39f-4c02-a8e2-82e56bd1610b

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Date deposited: 14 Jun 2005
Last modified: 17 Jul 2017 16:46

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