Complex linear models and cyclic designs

Designs for Generalized Linear Models

Optimal Designs for Generalized Linear Models

Both HK1 and HK2 deal with experiments in which the planned analysis is based on a linear model. Selecting designs for such experiments remains a critically important problem. However, there are many problems for which a linear model may not be a great choice. For example, if the response is a binary variable or a count variable rather than a continuous measurement, a linear model may be quite ...

Optimal Designs in Linear Regression Models

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Bayesian Designs for Hierarchical Linear Models

Two Bayesian optimal design criteria for hierarchical linear models are discussed – the ψβ criterion for the estimation of individual-level parameters β, and the ψθ criterion for the estimation of hyperparameters θ. While the ψβ criterion involves only the specification of the treatments, the ψθ criterion involves the specification of both the treatments and the covariates. We focus on a specif...

Sequential Experimental Designs for Generalized Linear Models

We consider the problem of experimental design when the response is modeled by a generalized linear model (GLM) and the experimental plan can be determined sequentially. Most previous research on this problem has been limited either to one-factor, binary response experiments or to augmenting the design when there are already sufficient data to compute parameter estimates. We suggest a new proce...