Asymptotic biases of information and cross-validation criteria under canonical parametrization

DOI10.1080/03610926.2017.1422759OpenAlexW2792051238MaRDI QIDQ5078294

Haruhiko Ogasawara

Publication date: 23 May 2022

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2017.1422759

zbMATH Keywords

infinitely divisibleAkaike information criterionexponential family of distributionsTakeuchi information criterionleave-\(k\)-out method

Mathematics Subject Classification ID

Multivariate distribution of statistics (62H10) Asymptotic properties of parametric estimators (62F12)

Cites Work

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• A class of cross-validatory model selection criteria
• Cross validation model selection criteria for linear regression based on the Kullback-Leibler discrepancy
• Bias correction of cross-validation criterion based on Kullback-Leibler information under a general condition
• Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models
• Cross-validation methods
• Extensions of Pearson's inequality between skewness and kurtosis to multivariate cases
• Corrected version of \(AIC\) for selecting multivariate normal linear regression models in a general nonnormal case
• Iterative Bias Correction of the Cross-Validation Criterion
• Bias Corrections of some Criteria for Selecting Multivariate Linear Models in a General Nonnormal Case
• Regression and time series model selection in small samples
• Modified AIC and Cp in multivariate linear regression
• ASYMPTOTIC CUMULANTS OF SOME INFORMATION CRITERIA
• Measures of multivariate skewness and kurtosis with applications
• Mean Square Error of Prediction as a Criterion for Selecting Variables
• On Information and Sufficiency

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