The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Valid post-selection inference
Open accessAbstract
It is common practice in statistical data analysis to perform data-driven variable selection and derive statistical inference from the resulting model. Such inference enjoys none of the guarantees that classical statistical theory provides for tests and confidence intervals when the model has been chosen a priori. We propose to produce valid “post-selection inference” by reducing the problem to one of simultaneous inference and hence suitably widening conventional confidence and retention intervals. Simultaneity is required for all linear functions that arise as coefficient estimates in all submodels. By purchasing “simultaneity insurance” for all possible submodels, the resulting post-selection inference is rendered universally valid under all possible model selection procedures. This inference is therefore generally conservative for particular selection procedures, but it is always less conservative than full Scheffé protection. Importantly it does not depend on the truth of the selected submodel, and hence it produces valid inference even in wrong models. We describe the structure of the simultaneous inference problem and give some asymptotic results.
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Matching Methods for Causal Inference: A Review and a Look Forward
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Angrist, J. D., & Pischke, J.-S. (2008) · doi:10.2307/j.ctvcm4j72 · Cited by 5,051
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Wager, S., & Athey, S. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1319839 · Cited by 2,917
Distribution-Free Predictive Inference for Regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1307116 · Cited by 1,049
Panning for Gold: ‘Model-X’ Knockoffs for High Dimensional Controlled Variable Selection
Candès, E., Fan, Y., Janson, L., & Lv, J. (2018) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12265 · Cited by 632
Pattern discovery and cancer gene identification in integrated cancer genomic data
Mo, Q., Wang, S., Seshan, V. E., Olshen, A. B., Schultz, N., Sander, C., Powers, R. S., Ladanyi, M., & Shen, R. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1208949110 · Cited by 474
Cross validation for model selection: A review with examples from ecology
Yates, L. A., Aandahl, Z., Richards, S. A., & Brook, B. W. (2023) · Ecological Monographs · doi:10.1002/ecm.1557 · Cited by 459
Metadata
- Authors
- Berk, R., Brown, L., Buja, A., Zhang, K., & Zhao, L.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2013-04-01
- Volume
- 41
- Issue
- 2
- Pages
- 802-837
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2009462809
- Cited by
- 599
- References
- 74
- FWCI
- 28.67
- Type
- journal article
- Language
- en
- Open access
- bronze
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