The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Super Learner
Open accessAbstract
When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many estimation procedures in their toolbox. A few examples of these candidate learners are: least squares, least angle regression, random forests, and spline regression. Previous articles (van der Laan and Dudoit (2003); van der Laan et al. (2006); Sinisi et al. (2007)) theoretically validated the use of cross validation to select an optimal learner among many candidate learners. Motivated by this use of cross validation, we propose a new prediction method for creating a weighted combination of many candidate learners to build the super learner. This article proposes a fast algorithm for constructing a super learner in prediction which uses V-fold cross-validation to select weights to combine an initial set of candidate learners. In addition, this paper contains a practical demonstration of the adaptivity of this so called super learner to various true data generating distributions. This approach for construction of a super learner generalizes to any parameter which can be defined as a minimizer of a loss function.
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Citations
Cites
Oracle inequalities for multi-fold cross validation
Vaart, A. W. V. D., Dudoit, S., & Laan, M. J. V. D. (2006) · Statistics & Decisions · doi:10.1524/stnd.2006.24.3.351 · Cited by 156 · cited 1×
“By a simple argument as presented in van der Laan and Dudoit (2003), Dudoit and van der Laan (2005) and van der Vaart et al. (2006), it follows that by letting the V = Vn in the V-fold cross-validation scheme converge to infinity at a slow enough rate relative to n, then either ψn = Ψ̂αn(Pn)…”
Self-citation
Cited by
Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297
Diagnosing and responding to violations in the positivity assumption
Petersen, M. L., Porter, K. E., Gruber, S., Wang, Y., & van der Laan, M. J. (2012) · Statistical Methods in Medical Research · doi:10.1177/0962280210386207 · Cited by 548
Self-citationThe promise of machine learning in predicting treatment outcomes in psychiatry
Chekroud, A. M., Bondar, J., Delgadillo, J., Doherty, G., Wasil, A., Fokkema, M., Cohen, Z., Belgrave, D., DeRubeis, R., Iniesta, R., et al. (2021) · World Psychiatry · doi:10.1002/wps.20882 · Cited by 531
Statistical analysis strategies for association studies involving rare variants
Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2867 · Cited by 455
Scapicchio, C., Gabelloni, M., Barucci, A., Cioni, D., Saba, L., & Neri, E. (2021) · La radiologia medica · doi:10.1007/s11547-021-01389-x · Cited by 435
Metadata
- Authors
- van der Laan, M. J., Polley, E. C., & Hubbard, A. E.
- Venue
- Statistical Applications in Genetics and Molecular Biology
- Publisher
- Walter de Gruyter GmbH
- Published
- 2007-09-16
- Volume
- 6
- Issue
- 1
- Pages
- Article25-Article25
- ISSN
- 1544-6115 (ISSN-L)
- 2194-6302
- OpenAlex
- W4233056867
- Cited by
- 1,719
- References
- 1
- FWCI
- 3.00
- Type
- journal article
- Language
- en
- Open access
- green
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- van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super Learner. Statistical Applications in Genetics and Molecular Biology. https://doi.org/10.2202/1544-6115.1309
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
