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Akaike's information criterion (AIC) has been developed by Hirotsugu Akaike as a measure of the goodness of fit of an estimated statistical model. It is grounded in the concept of entropy, in effect offering a relative measure of the information lost when a given model is used to describe reality and can be said to describe the tradeoff between bias and variance in model construction, or loosely speaking that of precision and complexity of the model. This is not a test of the model in the sense of hypothesis testing. Rather it is a tool for model selection. Given a dataset, several competing models may be ranked according to their AIC, with the one having the lowest AIC being the best.
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Manton, K., Akushevich, I., Kravchenko, J. (2009). U.S. Cancer Morbidity: Modeling Age-Patterns of Cancer Histotypes. In: Cancer Mortality and Morbidity Patterns in the U.S. Population. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-78193-8_7
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