Mario Trottini and Fulvio Spezzaferri
Given a sample of n i.i.d. observations generated from some unknown
distribution F, assume that F belongs to one of two parametric models
M1, M2, and that the estimation of the density of a future
observation is of interest. San Martini and Spezzaferri (1984)
proposed for this problem a predictive criterion based on the Kullback
Leibler divergence between the estimate and the density of the future
observation. In this paper a generalization of this criterion is
presented both using a general class of divergences and relaxing the
assumption that the true model belongs to M1 or M2.
Keywords: Model Selection, Gaussian Process, Loss Function, -divergences.