Model selection bias and Freedman's paradox

DOI10.1007/s10463-009-0234-4zbMath1422.62086OpenAlexW1980370194MaRDI QIDQ904076

David R. Anderson, Paul M. Lukacs, Kenneth P. Burnham

Publication date: 15 January 2016

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10463-009-0234-4

zbMATH Keywords

Akaike's information criterionmodel averagingmodel selection uncertaintyconfidence interval coverageFreedman's paradoxmodel selection biasmultimodel inferencestepwise selection

Mathematics Subject Classification ID

Statistical ranking and selection procedures (62F07)

Related Items (6)

Information‐theoretic model‐averaged benchmark dose analysis in environmental risk assessment ⋮ Model-Averaged Confidence Intervals ⋮ Predictive Modeling in Long-Term Care Insurance ⋮ Model-averaged profile likelihood intervals ⋮ Multimodel inference based on smoothed information criteria ⋮ Finite sample properties of confidence intervals centered on a model averaged estimator

Uses Software

• SAS

Cites Work

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• Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
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• Regression and time series model selection in small samples
• Inflation of R 2 in Best Subset Regression
• Further analysis of the data by Akaike's information criterion and the finite corrections
• A Bayesian extension of the minimum AIC procedure of autoregressive model fitting
• Model Selection: An Integral Part of Inference
• Model Selection and Multimodel Inference
• Model Based Inference in the Life Sciences: A Primer on Evidence

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