Continuous Optimal Designs for Generalized Linear Models under Model Uncertainty
- 4 Downloads
We propose a general design selection criterion for experiments where a generalized linear model describes the response. The criterion allows for several competing aims, such as parameter estimation and model discrimination, and also for uncertainty in the functional form of the linear predictor, the link function and the unknown model parameters. A general equivalence theorem is developed for this criterion. In practice, an exact design is required by experimenters and can be obtained by numerical rounding of a continuous design. We derive bounds on the performance of an exact design under this criterion which allow the efficiency of a rounded continuous design to be assessed.
Key-wordsExponential family General equivalence theorem Logistic regression Nonlinear regression Optimal design
AMS Subject Classification62K05 62J12
Unable to display preview. Download preview PDF.