Abstract
In this work we study the theory of optimal design of experiments when functional observations occur. We provide the best estimate for the functional coefficient in a linear model with functional response and multivariate predictor, exploiting fully the information provided by both functions and derivatives. We define different optimality criteria for the estimate of a functional coefficient. Then, we provide a strong theoretical foundation to prove that the computation of these optimal designs, in the case of linear models, is the same as in the classical theory, but a different interpretation needs to be given.
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Aletti, G., May, C., Tommasi, C. (2016). On Applying Optimal Design of Experiments when Functional Observations Occur. In: Kunert, J., Müller, C., Atkinson, A. (eds) mODa 11 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-31266-8_1
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DOI: https://doi.org/10.1007/978-3-319-31266-8_1
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