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.
2002
loss function; model selection; α-divergences
01 Pubblicazione su rivista::01a Articolo in rivista
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]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/19479
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