Designs for generalized linear models with several variables and model uncertainty

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

    Research output: Contribution to journalArticlepeer-review

    115 Citations (Scopus)


    Standard factorial designs sometimes may 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 that uses a criterion allowing 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.
    Original languageEnglish
    Pages (from-to)284-292
    Number of pages9
    Issue number2
    Publication statusPublished - May 2006


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