Model selection criteria based on Kullback information measures for nonlinear regression

DOI10.1016/j.jspi.2004.05.002zbMath1140.62331OpenAlexW2002890723MaRDI QIDQ2386146

Hyun-Joo Kim, Joseph E. Cavanaugh

Publication date: 22 August 2005

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2004.05.002

zbMATH Keywords

Akaike information criterionI-divergenceKullback-Leibler informationNonlinear regressionAICJ-divergence

Mathematics Subject Classification ID

General nonlinear regression (62J02) Statistical aspects of information-theoretic topics (62B10)

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Uses Software

• nlmdl

Cites Work

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• Unifying the derivations for the Akaike and corrected Akaike information criteria.
• A large-sample model selection criterion based on Kullback's symmetric divergence
• Regression and time series model selection in small samples
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Criteria for Linear Model Selection Based on Kullback's Symmetric Divergence
• On Information and Sufficiency
• A new look at the statistical model identification

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