Parsimonious model selection using information theory: a modified selection rule.
Ecology
; 102(10): e03475, 2021 10.
Article
en En
| MEDLINE
| ID: mdl-34272730
ABSTRACT
Information-theoretic approaches to model selection, such as Akaike's information criterion (AIC) and cross validation, provide a rigorous framework to select among candidate hypotheses in ecology, yet the persistent concern of overfitting undermines the interpretation of inferred processes. A common misconception is that overfitting is due to the choice of criterion or model score, despite research demonstrating that selection uncertainty associated with score estimation is the predominant influence. Here we introduce a novel selection rule that identifies a parsimonious model by directly accounting for estimation uncertainty, while still retaining an information-theoretic interpretation. The new rule, which is a modification of the existing one-standard-error rule, mitigates overfitting and reduces the likelihood that spurious effects will be included in the selected model, thereby improving its inferential properties. We present the rule and illustrative examples in the context of maximum-likelihood estimation and Kullback-Leibler discrepancy, although the rule is applicable in a more general setting, including Bayesian model selection and other types of discrepancy.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
/
Ecología
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Ecology
Año:
2021
Tipo del documento:
Article
País de afiliación:
Australia