Bias-Corrected AIC for Selecting Variables in Poisson Regression Models

DOI10.1080/03610926.2011.600504zbMath1268.62070OpenAlexW2032082883MaRDI QIDQ2839055

Ken-ichi Kamo, Kenichi Satoh, Hirokazu Yanagihara

Publication date: 4 July 2013

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

Full work available at URL: https://doi.org/10.1080/03610926.2011.600504

zbMATH Keywords

model selectionPoisson regressiongeneralized linear modelsAkaike's information criterion

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Generalized linear models (logistic models) (62J12)

Related Items (6)

Bias correction of the Akaike information criterion in factor analysis ⋮ Bias-corrected AIC for selecting variables in multinomial logistic regression models ⋮ ASYMPTOTIC CUMULANTS OF SOME INFORMATION CRITERIA ⋮ Simple Formula for Calculating Bias‐corrected AIC in Generalized Linear Models ⋮ Jackknife bias correction of the AIC for selecting variables in canonical correlation analysis under model misspecification ⋮ Non-parametric Poisson regression from independent and weakly dependent observations by model selection

Cites Work

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• Variable selection for the growth curve model
• Bias correction of AIC in logistic regression models
• Corrected versions of cross-validation criteria for selecting multivariate regression and growth curve models
• Corrected version of \(AIC\) for selecting multivariate normal linear regression models in a general nonnormal case
• Regression and time series model selection in small samples
• Further analysis of the data by Akaike's information criterion and the finite corrections
• Model Selection for Multivariate Regression in Small Samples
• On Information and Sufficiency

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