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.
Lingua originale | Inglese |
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Titolo della pubblicazione ospite | mODa 11 - Advances in Model-Oriented Design and Analysis |
Editore | SPRINGER |
Pagine | 1-9 |
Numero di pagine | 9 |
ISBN (stampa) | 978-3-319-31264-4 |
DOI | |
Stato di pubblicazione | Pubblicato - 1 gen 2016 |