In this paper some new properties and computational tools for finding KL-optimum designs are provided. KL-optimality is a general criterion useful to select the best experimental conditions to discriminate between statistical models. A KL-optimum design is obtained from a minimax optimization problem, which is defined on a infinite-dimensional space. In particular, continuity of the KL-optimality criterion is proved under mild conditions; as a consequence, the first-order algorithm converges to the set of KL-optimum designs for a large class of models. It is also shown that KL-optimum designs are invariant to any scale-position transformation. Some examples are given and discussed, together with some practical implications for numerical computation purposes.
KL-optimum designs: theoretical properties and practical computation / G. Aletti, C. May, C. Tommasi. - (2014 Sep 29).
|Titolo:||KL-optimum designs: theoretical properties and practical computation|
|Settore Scientifico Disciplinare:||Settore MAT/06 - Probabilita' e Statistica Matematica|
Settore SECS-S/01 - Statistica
|Data di pubblicazione:||2014-09-29|
|Appare nelle tipologie:||24 - Pre-print|