An asymptotic equivalence of choice of model by cross validation and Akaike's criterion
Abstract
Summary A logarithmic assessment of the performance of a predicting density is found to lead to asymptotic equivalence of choice of model by cross-validation and Akaike's criterion, when maximum likelihood estimation is used within each model.
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The performance of a model in k-fold cross-validation is estimated as the mean of the k validation results.
“The performance of the model on such a setup is estimated to be the mean of the k validation results [19,20,21].”
From Fully Automated Placental Volume Quantification From<scp>3D</scp>Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants (2022) · cites this paper as [21]
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- Authors
- Stone, M.
- Affiliations
- University College London (GB)
- Corresponding author
- Stone, M.
- Venue
- Journal of the Royal Statistical Society Series B (Statistical Methodology)
- Publisher
- Oxford University Press (OUP)
- Published
- 1977-09-01
- Volume
- 39
- Issue
- 1
- Pages
- 44-47
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W55848811
- Cited by
- 1,286
- References
- 4
- Citations by year
- 2026: 36 · 2025: 51 · 2024: 46 · 2023: 68 · 2022: 53 · 2021: 84 · 2020: 64 · 2019: 76 · 2018: 93 · 2017: 51 · 2016: 51 · 2015: 56 · 2014: 30 · 2013: 41 · 2012: 51
- FWCI
- 9.33
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- model selection, cross-validation, Akaike information criterion, asymptotic equivalence, predictive density, maximum likelihood estimation, logarithmic loss
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