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Cross-Validation of Regression Models
Abstract
A methodolgy for assessment of the predictive ability of regression models is presented. Attention is given to models obtained via subset selection procedures, which are extremely difficult to evaluate by standard techniques. Cross-validatory assessments of predictive ability are obtained and their use illustrated in examples.
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Cites
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Metadata
- Authors
- Picard, R. R., & Cook, R. D.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1984-09
- Volume
- 79
- Issue
- 387
- Pages
- 575-583
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2077562320
- Cited by
- 1,547
- References
- 2
- FWCI
- 1.75
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
