Controlling the error probabilities of model selection information criteria using bootstrapping
| Autor: | Scott Lidgard, Beckett Sterner, Michael Cullan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
021103 operations research business.industry Computer science Model selection 0211 other engineering and technologies Information Criteria 02 engineering and technology Machine learning computer.software_genre 01 natural sciences 010104 statistics & probability Null (SQL) Bootstrapping (electronics) V WCDANM 2018: Advances in Computational Data Analysis Artificial intelligence 0101 mathematics Statistics Probability and Uncertainty Akaike information criterion business computer Statistical hypothesis testing |
| Zdroj: | J Appl Stat |
| ISSN: | 1360-0532 0266-4763 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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