The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Stability Selection
Abstract
Summary: Estimation of structure, such as in variable selection, graphical modelling or cluster analysis, is notoriously difficult, especially for high dimensional data. We introduce stability selection. It is based on subsampling in combination with (high dimensional) selection algorithms. As such, the method is extremely general and has a very wide range of applicability. Stability selection provides finite sample control for some error rates of false discoveries and hence a transparent principle to choose a proper amount of regularization for structure estimation. Variable selection and structure estimation improve markedly for a range of selection methods if stability selection is applied. We prove for the randomized lasso that stability selection will be variable selection consistent even if the necessary conditions for consistency of the original lasso method are violated. We demonstrate stability selection for variable selection and Gaussian graphical modelling, using real and simulated data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
The adaptive lasso and its oracle properties
Zou, H. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000735 · Cited by 7,470
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980) · Wiley Series in Probability and Statistics · doi:10.1002/0471725153 · Cited by 7,297
Lauritzen, S. L. (1996) · doi:10.1093/oso/9780198522195.001.0001 · Cited by 2,976
Monti, S., Tamayo, P., Mesirov, J., & Golub, T. (2003) · Machine Learning · doi:10.1023/a:1023949509487 · Cited by 2,116
Integrating regulatory motif discovery and genome-wide expression analysis
Conlon, E. M., Liu, X. S., Lieb, J. D., & Liu, J. S. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0630591100 · Cited by 343
Cited by
Clinical and biological implications of driver mutations in myelodysplastic syndromes
Papaemmanuil, E., Gerstung, M., Malcovati, L., Tauro, S., Gundem, G., Van Loo, P., Yoon, C. J., Ellis, P., Wedge, D. C., Pellagatti, A., et al. (2013) · Blood · doi:10.1182/blood-2013-08-518886 · Cited by 1,832
Wisdom of crowds for robust gene network inference
The DREAM5 Consortium, Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2016 · Cited by 1,667
CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo
Moreno-Mateos, M. A., Vejnar, C. E., Beaudoin, J.-D., Fernandez, J. P., Mis, E. K., Khokha, M. K., & Giraldez, A. J. (2015) · Nature Methods · doi:10.1038/nmeth.3543 · Cited by 1,290
A simple new approach to variable selection in regression, with application to genetic fine mapping
Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12388 · Cited by 1,121
Kootte, R. S., Levin, E., Salojärvi, J., Smits, L. P., Hartstra, A. V., Udayappan, S. D., Hermes, G., Bouter, K. E., Koopen, A. M., Holst, J. J., et al. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.09.008 · Cited by 1,039
Metadata
- Authors
- Meinshausen, N., & Bühlmann, P.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-08-05
- Volume
- 72
- Issue
- 4
- Pages
- 417-473
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2562162676
- Cited by
- 2,215
- References
- 8
- FWCI
- 50.36
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Stability Selection. https://app.cochat.ai/papers/10.1111/j.1467-9868.2010.00740.x
- The paper
- Meinshausen, N., & Bühlmann, P. (2010). Stability Selection. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/j.1467-9868.2010.00740.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
