A large-sample model selection criterion based on Kullback's symmetric divergence

DOI10.1016/S0167-7152(98)00200-4zbMath0955.62012OpenAlexW1966038674WikidataQ127305850 ScholiaQ127305850MaRDI QIDQ1962213

Joseph E. Cavanaugh

Publication date: 27 February 2001

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(98)00200-4

zbMATH Keywords

relative entropyAkaike information criterionI-divergenceKullback-Leibler informationsimulationsAICJ-divergence

Mathematics Subject Classification ID

Statistical aspects of information-theoretic topics (62B10)

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Cites Work

• Modeling by shortest data description
• Asymptotically efficient selection of the order of the model for estimating parameters of a linear process
• Estimating the dimension of a model
• Unifying the derivations for the Akaike and corrected Akaike information criteria.
• Fitting autoregressive models for prediction
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• ON THE UNBIASEDNESS PROPERTY OF AIC FOR EXACT OR APPROXIMATING LINEAR STOCHASTIC TIME SERIES MODELS
• How Biased is the Apparent Error Rate of a Prediction Rule?
• Regression and time series model selection in small samples
• Some recent advances in time series modeling
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Assessing the accuracy of the maximum likelihood estimator: Observed versus expected Fisher information
• Model Selection for Multivariate Regression in Small Samples
• A CORRECTED AKAIKE INFORMATION CRITERION FOR VECTOR AUTOREGRESSIVE MODEL SELECTION
• Some Comments on C P
• On Information and Sufficiency
• An invariant form for the prior probability in estimation problems
• A new look at the statistical model identification
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• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

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