Cross validation model selection criteria for linear regression based on the Kullback-Leibler discrepancy

DOI10.1016/j.stamet.2005.05.002zbMath1248.62110OpenAlexW2093725103MaRDI QIDQ713660

Joseph E. Cavanaugh, Simon L. Davies, Andrew A. Neath

Publication date: 19 October 2012

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.stamet.2005.05.002

zbMATH Keywords

Akaike information criterionKullback-Leibler informationAICAICC

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Statistical aspects of information-theoretic topics (62B10)

Related Items (6)

The connection between cross-validation and Akaike information criterion in a semiparametric family ⋮ Model selection criteria based on cross-validatory concordance statistics ⋮ Asymptotic biases of information and cross-validation criteria under canonical parametrization ⋮ Bootstrap-based model selection criteria for beta regressions ⋮ A survey of cross-validation procedures for model selection ⋮ An alternate approach to pseudo-likelihood model selection in the generalized linear mixed modeling framework

Cites Work

• Unifying the derivations for the Akaike and corrected Akaike information criteria.
• Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation
• How Biased is the Apparent Error Rate of a Prediction Rule?
• Regression and time series model selection in small samples
• Further analysis of the data by Akaike's information criterion and the finite corrections
• The Relationship between Variable Selection and Data Agumentation and a Method for Prediction
• Linear Model Selection by Cross-Validation
• Approximations for the Psi (Digamma) Function
• Robust Statistics
• A new look at the statistical model identification
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