The DOI matches its Crossref record.
Frequency of Selecting Noise Variables in Subset Regression Analysis: A Simulation Study
Abstract
This article presents the results of a simulation study of variable selection in a multiple regression context that evaluates the frequency of selecting noise variables and the bias of the adjusted R 2 of the selected variables when some of the candidate variables are authentic. It is demonstrated that for most samples a large percentage of the selected variables is noise, particularly when the number of candidate variables is large relative to the number of observations. The adjusted R 2 of the selected variables is highly inflated.
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Citations
Cited by
Causation, Prediction, and Search
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Mac Nally, R. (2000) · Biodiversity & Conservation · doi:10.1023/a:1008985925162 · Cited by 938
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Sun, G.-W., Shook, T. L., & Kay, G. L. (1996) · Journal of Clinical Epidemiology · doi:10.1016/0895-4356(96)00025-x · Cited by 933
Bootstrap Methods for Developing Predictive Models
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Metadata
- Authors
- Flack, V. F., & Chang, P. C.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1987-02
- Volume
- 41
- Issue
- 1
- Pages
- 84-86
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2089262842
- Cited by
- 165
- References
- 0
- FWCI
- 3.79
- Type
- journal article
- Language
- en
- Open access
- closed
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- Flack, V. F., & Chang, P. C. (1987). Frequency of Selecting Noise Variables in Subset Regression Analysis: A Simulation Study. The American Statistician. https://doi.org/10.1080/00031305.1987.10475450
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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.
