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.


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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
Subjects: H Social Sciences > HA Statistics
Divisions : University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
ePrint ID: 19342
Accepted Date and Publication Date:
8 February 2006Made publicly available
Date Deposited: 08 Feb 2006
Last Modified: 31 Mar 2016 11:36

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