D-OPTIMAL DESIGNS FOR POISSON REGRESSION MODELS

K. G. Russell · David C. Woods · S. M. Lewis · John A Eccleston

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Optimal Experimental Design Methods Simulation Techniques and Applications Advanced Statistical Methods and Models

Abstract

We consider the problem of finding an optimal design under a Poisson regression model with a log link, any number of independent variables, and an additive linear predictor. Local D-optimality of a class of designs is established through use of a canonical form of the problem and a general equivalence theorem. The results are applied in conjunction with clustering techniques to obtain a fast method of finding designs that are robust to wide ranges of model parameter values. The methods are illustrated through examples.Key words and phrases: Clustering, locally optimal design, log-linear models, robust design.

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References

1993Optimal design of experiments 2007Optimum Experimental Designs, with SAS 1989Optimal Bayesian design applied to logistic regression experiments 1992The Use of a Canonical Form in the Construction of Locally Optimal Designs for Non-Linear Problems 2006Designs for Generalized Linear Models With Several Variables and Model Uncertainty 2006Robust Experimental Design for Multivariate Generalized Linear Models 1992Robust Designs for Binary Data

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