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Designs for dependent observations

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Design and Analysis of Experiments

We consider the problem of optimal experimental design for the regression models on an interval, where the observations are correlated and the errors come from either Markov or conditionally Markov process. We study transformations of these regression models and corresponding designs. We show, in particular, that in many cases we can assume that the underlying model of errors is the Brownian motion.

This is a joint work with H. Dette and A. Pepelyshev.

This talk is part of the Isaac Newton Institute Seminar Series series.

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