On distribution of AIC in linear regression models

DOI10.1016/j.jspi.2004.03.016zbMath1065.62019OpenAlexW2003265874MaRDI QIDQ1781523

Chihiro Ohmoto, Hirokazu Yanagihara

Publication date: 27 June 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.2004.03.016

zbMATH Keywords

Akaike information criterionConfidence intervalMaximum likelihood estimatorAsymptotic expansionAsymptotic distributionAsymptotic varianceNon-centrality parameterAsymptotic kurtosisAsymptotic skewnessLinear regression model

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Statistical aspects of information-theoretic topics (62B10)

Related Items (7)

Inference after separated hypotheses testing: an empirical investigation for linear models ⋮ ASYMPTOTIC CUMULANTS OF SOME INFORMATION CRITERIA ⋮ Tracking interval for selecting between non-nested models: an investigation for type II right censored data ⋮ Linear Signed Rank Test for Model Selection ⋮ Adjusting Stepwisep-Values in Generalized Linear Models ⋮ Unbiased estimates for moments and cumulants in linear regression ⋮ Empiricial Comparison between Some Model Selection Criteria

Cites Work

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• Further analysis of the data by Akaike's information criterion and the finite corrections
• Modified AIC and Cp in multivariate linear regression
• ASYMPTOTIC APPROXIMATIONS OF THE NULL DISTRIBUTION OF THE ONE-WAY ANOVA TEST STATISTIC UNDER NONNORMALITY
• Model Selection for Multivariate Regression in Small Samples
• The bootstrap and Edgeworth expansion
• Asymptotic expansions of the null distributions of three test statistics in a nonnormal GMANOVA model

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