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).

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Description/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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1198/004017005000000571
ISSNs: 0040-1706 (print)
Keywords: binary response, d-optimality, logistic regression, robust design, simulation
Subjects: Q Science > QA Mathematics
H Social Sciences > HA Statistics
Organisations: Statistics
ePrint ID: 15828
Date :
Date Event
2006Published
Date Deposited: 14 Jun 2005
Last Modified: 16 Apr 2017 23:27
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/15828

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