Simple Formula for Calculating Bias‐corrected AIC in Generalized Linear Models

DOI10.1111/sjos.12049zbMath1416.62426OpenAlexW1882506292MaRDI QIDQ5418641

Hirofumi Wakaki, Hirokazu Yanagihara, Shinpei Imori

Publication date: 26 May 2014

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: http://ir.lib.hiroshima-u.ac.jp/files/public/3/36453/20141203091247754904/k6493_3.pdf

zbMATH Keywords

maximum likelihood estimationstochastic expansionvariable selectioninformation criterion

Mathematics Subject Classification ID

Generalized linear models (logistic models) (62J12) Statistical aspects of information-theoretic topics (62B10)

Related Items (2)

ASYMPTOTIC CUMULANTS OF SOME INFORMATION CRITERIA ⋮ Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under model misspecification

Uses Software

• R

Cites Work

• Bias-corrected AIC for selecting variables in multinomial logistic regression models
• Consistency and asymptotic normality of the maximum likelihood estimator in generalized linear models
• Asymptotically efficient selection of the order of the model for estimating parameters of a linear process
• Bias correction of AIC in logistic regression models
• Bias-Corrected AIC for Selecting Variables in Poisson Regression Models
• Regression and time series model selection in small samples
• A note on the corrected Akaike information criterion for threshold autoregressive models
• Further analysis of the data by Akaike's information criterion and the finite corrections
• On Information and Sufficiency

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