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• W2994876975 abstract "The Akaike Information Criterion (AIC) and related information criteria are powerful and increasingly popular tools for comparing multiple, non-nested models without the specification of a null model. However, existing procedures for information-theoretic model selection do not provide explicit and uniform control over error rates for the choice between models, a key feature of classical hypothesis testing. We show how to extend notions of Type-I and Type-II error to more than two models without requiring a null. We then present the Error Control for Information Criteria (ECIC) method, a bootstrap approach to controlling Type-I error using Difference of Goodness of Fit (DGOF) distributions. We apply ECIC to empirical and simulated data in time series and regression contexts to illustrate its value for parametric Neyman-Pearson classification. An R package implementing the bootstrap method is publicly available." @default.
• W2994876975 created "2019-12-26" @default.
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• W2994876975 date "2019-12-18" @default.
• W2994876975 modified "2023-10-17" @default.
• W2994876975 title "Controlling the error probabilities of model selection information criteria using bootstrapping" @default.
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• W2994876975 doi "https://doi.org/10.1080/02664763.2019.1701636" @default.
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