Designs for generalized linear models with several variables and model uncertainty

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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.