Model-robust and model-sensitive designs

Model-robust and model-sensitive designs

The main drawback of the optimal design approach is that it assumes the statistical model is known. In this paper, a new approach to reduce the dependency on the assumed model is proposed. The approach takes into account the model uncertainty by incorporating the bias in the design criterion and the ability to test for lack-of-fit. Several new designs are derived in the paper and they are compa...

Model-Robust Factorial Designs

Model-sensitive sequential optimal designs

The increasing number of experimenters using computer-generated experimental designs creates an increasing need to have design procedures that are less sensitive to model misspecification. To address this problem, the notion of empirical models that have both important and potential terms is used. A two-stage design strategy for planning experiments in the face of model uncertainty is proposed....

Robust model-based sampling designs

I will describe some work currently being carried out with Alan Welsh at Australian National University. The problem addressed is to draw a sample, from which to estimate a population total. The data are completely known covariates, to which the unknown response variable is related. Difficulties to be overcome are that the relationship between these variables is only approximately, and perhaps ...

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 ...