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Akaike Information Criterion (AIC) is a way of selecting a
model from a set of models.
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Information-theoretic approaches provide methods for
model selection and (multi)
model inference that differ quite a bit from more traditional methods based on null hypothesis testing (e.g., Anderson, 2007; Burnham & Anderson, 2002).
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information-theoretic ideas had been developed at Bell Labs, all implicitly assuming ...
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Akaike information criterion (AIC) is an estimator of the relative quality of statistical models for a given set of data. Given a collection of models for the data, AIC estimates the quality of each
model, relative to each of the other models. Thus, AIC provides a means for
model selection.. AIC is founded on information theory.When a statistical
model is used to represent the process that ... B.e.s.t Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach Download Online
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