Among optimality criteria adopted to select best experimental designs to discriminate between different models, the KL-optimality criterion is very general. A KL-optimum design is obtained from a minimax optimization problem on an infinite-dimensional space. In this paper some important properties of the KL-optimality criterion function are highlighted and an algorithm to construct a KL-optimum design is proposed. It is analytically proved that a sequence of designs obtained by iteratively applying this algorithm converges to the set of KL-optimum designs, provided that the designs are regular. Furthermore a regularization procedure is discussed.
A Convergent Algorithm for Finding KL-Optimum Designs and Related Properties / G. Aletti, C. May, C. Tommasi - In: mODa 10 – Advances in Model-Oriented Design and Analysis / [a cura di] D. Ucinski, A.C. Atkinson, M. Patan. - [s.l] : Springer, 2013. - ISBN 978-3-319-00217-0. - pp. 1-9 (( Intervento presentato al 10. convegno International Workshop in Model-Oriented Design and Analysis tenutosi a Łagów Lubuski, Poland nel 2013 [10.1007/978-3-319-00218-7_1].
A Convergent Algorithm for Finding KL-Optimum Designs and Related Properties
G. AlettiPrimo
;C. MaySecondo
;C. TommasiUltimo
2013
Abstract
Among optimality criteria adopted to select best experimental designs to discriminate between different models, the KL-optimality criterion is very general. A KL-optimum design is obtained from a minimax optimization problem on an infinite-dimensional space. In this paper some important properties of the KL-optimality criterion function are highlighted and an algorithm to construct a KL-optimum design is proposed. It is analytically proved that a sequence of designs obtained by iteratively applying this algorithm converges to the set of KL-optimum designs, provided that the designs are regular. Furthermore a regularization procedure is discussed.Pubblicazioni consigliate
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