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A Note On the Unification of the Akaike Information Criterion

by Peide Shi, P. R. China, Chih-ling Tsai - J. Royal Statist. Soc. (B , 1998
"... this paper is first to propose a generalized Kullback-Leibler information that can measure the discrepancy between a robust function evaluated under both the true model and fitted models. Next, we use this generalized Kullback-Leibler information to obtain three generalized Akaike information criter ..."
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this paper is first to propose a generalized Kullback-Leibler information that can measure the discrepancy between a robust function evaluated under both the true model and fitted models. Next, we use this generalized Kullback-Leibler information to obtain three generalized Akaike information

Kullback-Leibler Information Criterion

by Raffaella Giacomini, Barbara Rossi, We Also Thank B. Hansen, M. Jacoviello, U. Muller, M. Del Negro, G. Primiceri, T. Zha , 2007
"... We propose new methods for analyzing the relative performance of two competing, misspecified models in the presence of possible data instability. The main idea is to develop a measure of the relative “local performance ” for the two models, and to investigate its stability over time by means of stat ..."
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of statistical tests. The models ’ performance can be evaluated using either in-sample or out-of-sample criteria. In the former case, we suggest using the local Kullback-Leibler information criterion, whereas in the latter, we consider the local out-of-sample forecast loss, for a general loss function. We

Rényi Divergence and Kullback-Leibler Divergence

by Tim Erven
"... Abstract—Rényi divergence is related to Rényi entropy much like Kullback-Leibler divergence is related to Shannon’s entropy, and comes up in many settings. It was introduced by Rényi as a measure of information that satisfies almost the same axioms as Kullback-Leibler divergence, and depends on a ..."
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Abstract—Rényi divergence is related to Rényi entropy much like Kullback-Leibler divergence is related to Shannon’s entropy, and comes up in many settings. It was introduced by Rényi as a measure of information that satisfies almost the same axioms as Kullback-Leibler divergence, and depends

Bootstrap estimate of Kullback-Leibler information for model selection

by Ritei Shibata, Ritei Shibata - Statistica Sinica , 1997
"... Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical model selection procedure which is based on likelihood principle like AIC. To discriminate nested models, we have to estimate it up to the order of constant while the Kullback-Leibler information itself ..."
Abstract - Cited by 27 (0 self) - Add to MetaCart
Estimation of Kullback-Leibler amount of information is a crucial part of deriving a statistical model selection procedure which is based on likelihood principle like AIC. To discriminate nested models, we have to estimate it up to the order of constant while the Kullback-Leibler information itself

Relations between Kullback-Leibler distance and Fisher information

by Anand G. Dabak, Don H. Johnson , 2002
"... The Kullback-Leibler distance between two probability densities that are parametric perturbations of each other is related to the Fisher information. We generalize this relationship to the case when the perturbations may not be small and when the two densities are non-parametric. ..."
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The Kullback-Leibler distance between two probability densities that are parametric perturbations of each other is related to the Fisher information. We generalize this relationship to the case when the perturbations may not be small and when the two densities are non-parametric.

Kullback-Leibler approximation of spectral density functions

by Tryphon T. Georgiou, Anders Lindquist - IEEE Trans. Inform. Theory , 2003
"... Abstract—We introduce a Kullback–Leibler-type distance between spectral density functions of stationary stochastic processes and solve the problem of optimal approximation of a given spectral density 9 by one that is consistent with prescribed second-order statistics. In general, such statistics are ..."
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—Approximation of power spectra, cross-entropy minimization, Kullback–Leibler distance, mutual information, optimization, spectral estimation. I.

PLANS D’INFORMATION DE KULLBACK-LEIBLER MINIMALE

by Astrid Jourdan , 2008
"... Depuis quelques années, la simulation numérique modélise des phénomènes toujours plus complexes. De tels problèmes, généralement de très grande dimension, exigent des codes de simulation sophistiqués et très coûteux en temps ..."
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Depuis quelques années, la simulation numérique modélise des phénomènes toujours plus complexes. De tels problèmes, généralement de très grande dimension, exigent des codes de simulation sophistiqués et très coûteux en temps

Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance

by Minh N. Do, Martin Vetterli - IEEE Trans. Image Processing , 2002
"... We present a statistical view of the texture retrieval problem by combining the two related tasks, namely feature extraction (FE) and similarity measurement (SM), into a joint modeling and classification scheme. We show that using a consistent estimator of texture model parameters for the FE step fo ..."
Abstract - Cited by 241 (4 self) - Add to MetaCart
followed by computing the Kullback--Leibler distance (KLD) between estimated models for the SM step is asymptotically optimal in term of retrieval error probability. The statistical scheme leads to a new wavelet-based texture retrieval method that is based on the accurate modeling of the marginal

Kullback-Leibler Boosting

by Ce Liu , Hueng-Yueng Shum
"... In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting ..."
Abstract - Cited by 68 (2 self) - Add to MetaCart
In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting

Symmetrizing the Kullback-Leibler Distance

by Don H. Johnson, Sinan Sinanovic - IEEE Transactions on Information Theory , 2000
"... We define a new distance measure the resistor-average distance between two probability distributions that is closely related to the Kullback-Leibler distance. While the KullbackLeibler distance is asymmetric in the two distributions, the resistor-average distance is not. It arises from geometric ..."
Abstract - Cited by 42 (0 self) - Add to MetaCart
information-theoretic "distance" measure from a viewpoint of theory. If p 0 , p 1 are two probability densities, the KullbackLeibler distance is defined to be D(p 1 #p 0 )= # p 1 (x)log p 1 (x) p 0 (x) dx . (1) In this paper, log() has base two. The Kullback-Leibler distance is but one example
Results 1 - 10 of 87,538