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- W2183642388 abstract "Akaike’s information-theoretic criterion (AIC) formodel discrimination [1] is often stated to “overfit”, i.e., it selects models with a higher dimension than the dimension of the model that generated the data. However, when no fixed-dimensional correct model exists, for example for pharmacokinetic data, AIC (or its biascorrected version AICc) might be the selection criterion of choice if the objective is to minimize prediction error [2, 3]. The present simulation study was designed to assess the behavior of AIC and other criteria under this type of model misspecification, for various sample sizes and measurement noise levels." @default.
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- W2183642388 date "2007-01-01" @default.
- W2183642388 modified "2023-09-27" @default.
- W2183642388 title "The performance of model selection criteria in the absence of a fixed-dimensional correct model" @default.
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