Error Statistics Using the Akaike and Bayesian Information Criteria.
Many biologists, especially in ecology and evolution, analyze their data by estimating fits to a set of candidate models and selecting the best model according to the Akaike Information Criterion (AIC) or the Bayesian Information Criteria (BIC). When the candidate models represent alternative hypoth...
| Publicado en: | Erkenntnis Vol. 91; no. 1; pp. 379 - 409 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Jan2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=190712037&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 190712037 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01650106 5KZ jtl: Erkenntnis issn: 01650106 maglogo: N pubinfo: dt: Jan2026 vid: 91 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 190712037 10.1007/s10670-024-00897-2 ppf: 379 ppct: 30 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.3MB tig: atl: Error Statistics Using the Akaike and Bayesian Information Criteria. aug: au: Cheng, Henrique Sterner, Beckett affil: https://ror.org/04wzb3z02 Exponent, Inc., 23445 North 19th Ave, 85027, Phoenix, AZ, USA https://ror.org/03efmqc40 School of Life Sciences, Arizona State University, 427 East Tyler Mall, 85281, Tempe, AZ, USA su: Akaike information criterion Neyman-Pearson theorem Statistical errors Philosophy of science Life sciences sug: subj: Akaike information criterion Neyman-Pearson theorem Statistical errors Philosophy of science Life sciences ab: Many biologists, especially in ecology and evolution, analyze their data by estimating fits to a set of candidate models and selecting the best model according to the Akaike Information Criterion (AIC) or the Bayesian Information Criteria (BIC). When the candidate models represent alternative hypotheses, biologists may want to limit the chance of a false positive to a specified level. Existing model selection methodology, however, allows for only indirect control over error rates by setting a threshold for the difference in AIC scores. We present a novel theoretical framework for parametric Neyman-Pearson (NP) model selection using information criteria that does not require a pre-data null and applies to three or more non-nested models simultaneously. We apply the theoretical framework to the Error Control for Information Criteria (ECIC) procedure introduced by Cullan et al. (J Appl Stat 47: 2565–2581, 2019), and we show it shares many of the desirable properties of AIC-type methods, including false positive and negative rates that converge to zero asymptotically. We discuss implications for the compatibility of evidentialist and severity-based approach to evidence in philosophy of science. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Erkenntnis is a copyright of Springer, 2026. All Rights Reserved. item: Erkenntnis holder: Springer Nature dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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