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      AIC model selection using Akaike weights

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      Psychonomic Bulletin & Review
      Springer Nature

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          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.

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          Bayes Factors

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            Further analysts of the data by akaike' s information criterion and the finite corrections

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              Bayesian Model Selection and Model Averaging.

              This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss implementation details, approximations, and relationships to other methods. Copyright 2000 Academic Press.
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                Author and article information

                Journal
                Psychonomic Bulletin & Review
                Psychonomic Bulletin & Review
                Springer Nature
                1069-9384
                1531-5320
                February 2004
                February 2004
                : 11
                : 1
                : 192-196
                Article
                10.3758/BF03206482
                15117008
                2e3a1bb3-912d-4610-b727-8ac3bce96457
                © 2004
                History

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