Definition
Akaike’s Information Criterion (AIC) is a technique that measures the goodness of an estimated statistical model and selects a model from a set of candidate models. The chosen model is the one that is expected to minimize the difference between the model and the truth. Given a data set, several competing models may be ranked according to their corresponding AIC, and the one having the lowest AIC will be the best.
In the general case, the AIC is defined as
where \( k \) is the number of parameters in the statistical model, and \( L \) is the maximized value of the likelihood function for the estimated model.
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References
Akaike H. A new look at the statistical model identification. IEEE Trans Autom Contr. 1974;19(6):716–23
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Xu, K. (2013). Akaike’s Information Criterion (AIC). In: Dubitzky, W., Wolkenhauer, O., Cho, KH., Yokota, H. (eds) Encyclopedia of Systems Biology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9863-7_458
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DOI: https://doi.org/10.1007/978-1-4419-9863-7_458
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