Controlling the error probabilities of model selection information criteria using bootstrapping.
J Appl Stat
; 47(13-15): 2565-2581, 2020.
Article
em En
| MEDLINE
| ID: mdl-35707440
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
J Appl Stat
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Estados Unidos