Accounting for Heteroscedasticity in Experimental Design

Abstract
Power transformation weighting has been found to be a powerful technique for accounting for heteroscedasticity in model fitting. In this paper the transformation weighting concept is used in developing sequential design criteria for precise parameter estimation in heteroscedastic situations. Criteria are proposed for precise estimation of the model and transformation parameters together and for precise estimation of the model parameters alone. Implementation of the criteria is illustrated with two examples from chemical kinetics.

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