Given a random sample from some unknown model belonging to a finite class of parametric models, assume that the estimate of the density of a future observation is of interest. San Martini & Spezzaferri (1984) proposed for this problem a predictive criterion based on the logarithmic utility function. The present authors investigate a generalization of this criterion that uses as a loss function an element of the class of alpha-divergences discussed by Ali & Silvey (1966) and Csiszar (1967). They also discuss briefly the case in which the class of models considered is not exhaustive.
A generalized predictive criterion for model selection / Mario, Trottini; Spezzaferri, Fulvio. - In: CANADIAN JOURNAL OF STATISTICS. - ISSN 0319-5724. - STAMPA. - 30:1(2002), pp. 79-96. [10.2307/3315866]
A generalized predictive criterion for model selection
SPEZZAFERRI, Fulvio
2002
Abstract
Given a random sample from some unknown model belonging to a finite class of parametric models, assume that the estimate of the density of a future observation is of interest. San Martini & Spezzaferri (1984) proposed for this problem a predictive criterion based on the logarithmic utility function. The present authors investigate a generalization of this criterion that uses as a loss function an element of the class of alpha-divergences discussed by Ali & Silvey (1966) and Csiszar (1967). They also discuss briefly the case in which the class of models considered is not exhaustive.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.