AIC model selection using Akaike weights

Eric Jan Wagenmakers, Simon Farrell

Research output: Chapter in Book/Conference paperChapter

1181 Citations (Scopus)

Abstract

The Akaike information criterion (AIC; Akaike, 1973) is a popular method for comparing the adequacy of multiple, possibly nonnested models. Current practice in cognitive psychology is to accept a single model on the basis of only the "raw" AIC values, making it difficult to unambiguously interpret the observed AIC differences in terms of a continuous measure such as probability. Here we demonstrate that AIC values can be easily transformed to so-called Akaike weights (e.g., Akaike, 1978, 1979; Bozdogan, 1987; Burnham & Anderson, 2002), which can be directly interpreted as conditional probabilities for each model. We show by example how these Akaike weights can greatly facilitate the interpretation of the results of AIC model comparison procedures.
Original languageEnglish
Title of host publicationPsychonomic Bulletin and Review
PublisherPsychonomic Society
Pages192-196
Number of pages5
ISBN (Print)1069-9384
DOIs
Publication statusPublished - 2004
Externally publishedYes

Publication series

NamePsychonomic Bulletin and Review
Volume11

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