# name
On the relationships between \alpha-connections and the asymptotic properties of predictive distributions

# metadata.dc.description[].value
Preprint enviat per a la seva publicació en una revista científica: Bernoulli, 1999, vol. 5, núm. 1, p. 163-176. [http://projecteuclid.org/euclid.bj/1173707099]

# metadata.dc.description.abstract[].value
In a recent paper Komaki studies the second-order asymptotic properties
of the predictive distributions, using the Kullback-Leibler divergence
as loss function. He shows that estimative distributions with asymptotically
efficient estimators can be improved by predictive distributions
that do not belong to the model. The model is assumed to be a multidimensional
curved exponential family. In this paper we generalize the
result assuming as loss function any f-divergence. It appears a relationship
between the a-connections and the optimal predictive distributions.
In particular, using an a-divergence to measure the goodness of a predictive
distribution, the optimal shift of the estimative distribution is related
with alpha-covariant derivatives. The expression we obtain for the asymptotic
risk is also useful to study the higher-order asymptotic properties of
an estimator, in the mentioned class of loss functions.

# metadata.dc.relation.isformatof[].value
Reproducció digital del document original en paper [CRAI Biblioteca de Matemàtiques i Informàtica - Dipòsit Departament CAIXA 37.1]

# metadata.dc.source[].value
Preprints de Matemàtiques - Mathematics Preprint Series

# metadata.dc.subject.other[].value
Universitat de Barcelona. Institut de Matemàtica

# metadata.dc.title[].value
On the relationships between \alpha-connections and the asymptotic properties of predictive distributions