Robust experimental designs for model calibration

Exchange Algorithms for Constructing Model-Robust Experimental Designs

S INCE Kiefer (1959) debuted the idea of optimal design of experiments, a vast literature has grown up around the notion of choosing a design based on some numerical criterion. The most common is D-optimality, which chooses the design minimizing the generalized variance of the regression parameter estimates. Though standard designs can be used in most design situations, optimal procedures are u...

Model-Robust Factorial Designs

Model-robust designs for split-plot experiments

Split-plot experiments are appropriate when some factors are more difficult and/or expensive to change than others. They require two levels of randomization resulting in a non-independent error structure. The design of such experiments has garnered much recent attention, including work on exact D-optimal split-plot designs. However, many of these procedures rely on the a priori assumption that ...

Model robust designs for survival trials

The exponential-based proportional hazards model is often assumed in timeto-event experiments but may only approximately hold. We consider deviations in different neighbourhoods of this model that include other widely used parametric proportional hazards models and we further assume that the data are subject to censoring. Minimax designs are then found explicitly based on criteria corresponding...

Model-Robust Designs for Quantile Regression

We give methods for the construction of designs for regression models, when the purpose of the investigation is the estimation of the conditional quantile function, and the estimation method is quantile regression. The designs are robust against misspecified response functions, and against unanticipated heteroscedasticity. The methods are illustrated by example, and in a case study in which the...