A K -fold averaging cross-validation procedure
Open accessAbstract
Cross-validation (CV) type of methods have been widely used to facilitate model estimation and variable selection. In this work, we suggest a new K-fold CV procedure to select a candidate ‘optimal’ model from each hold-out fold and average the K candidate ‘optimal’ models to obtain the ultimate model. Due to the averaging effect, the variance of the proposed estimates can be significantly reduced. This new procedure results in more stable and efficient parameter estimation than the classical K-fold CV procedure. In addition, we show the asymptotic equivalence between the proposed and classical CV procedures in the linear regression setting. We also demonstrate the broad applicability of the proposed procedure via two examples of parameter sparsity regularisation and quantile smoothing splines modelling. We illustrate the promise of the proposed method through simulations and a real data example.
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A 10-fold stratified cross-validation procedure was employed during development.
“A 10-fold stratified cross-validation procedure was employed during development”
From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as Jung and Hu, 2015
Section 4 (Simulations; page not provided) considers 10 folds and describes K-fold cross-validation, supporting the 10-fold part of the method. It specifies a random, even split but does not describe stratification. The source also cannot establish what the citing authors employed during their own development. (LLM, conf=0.86)
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Hudon, A. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1606250 · Cited by 3 · see citing works
- Authors
- Jung, Y., & Hu, J.
- Affiliations
- University of Waikato (NZ)
- The University of Texas MD Anderson Cancer Center (US)
- Corresponding author
- Jung, Yoonsuh
- Venue
- Journal of Nonparametric Statistics
- Publisher
- Informa UK Limited
- Published
- 2015-02-26
- Volume
- 27
- Issue
- 2
- Pages
- 167-179
- ISSN
- 1026-7654 (ISSN-L)
- 1048-5252
- 1029-0311
- PMC
- PMC5019184
- OpenAlex
- W2000515020
- Cited by
- 401
- References
- 2
- Citations by year
- 2026: 35 · 2025: 57 · 2024: 67 · 2023: 58 · 2022: 52 · 2021: 41 · 2020: 41 · 2019: 25 · 2018: 14 · 2017: 5 · 2016: 5 · 2015: 1
- FWCI
- 7.42
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of nonparametric statistics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Research Commons (University of Waikato) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Cancer Institute (R01CA158113, P30CA016672, U24CA086368)
- National Institute of General Medical Sciences (R01GM080503)
- 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
- k-fold cross-validation, model selection, variance reduction, parameter estimation, linear regression, asymptotic equivalence
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