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The Hat Matrix in Regression and ANOVA
Open accessAbstract
In least-squares fitting it is important to understand the influence which a data y value will have on each fitted y value. A projection matrix known as the hat matrix contains this information and, together with the Studentized residuals, provides a means of identifying exceptional data points. This approach also simplifies the calculations involved in removing a data point, and it requires only simple modifications in the preferred numerical least-squares algorithms.
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Cites
Solving Least Squares Problems
Lawson, C. L., & Hanson, R. J. (1995) · doi:10.1137/1.9781611971217 · Cited by 7,103
The Relationship Between Variable Selection and Data Agumentation and a Method for Prediction
Allen, D. M. (1974) · Technometrics · doi:10.1080/00401706.1974.10489157 · Cited by 1,469
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Accurate Model Selection of Relaxed Molecular Clocks in Bayesian Phylogenetics
Baele, G., Li, W. L. S., Drummond, A. J., Suchard, M. A., & Lemey, P. (2012) · Molecular Biology and Evolution · doi:10.1093/molbev/mss243 · Cited by 594
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Metadata
- Authors
- Hoaglin, D. C., & Welsch, R. E.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 1978-02
- Volume
- 32
- Issue
- 1
- Pages
- 17-22
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W1983350937
- Cited by
- 912
- References
- 2
- FWCI
- 14.13
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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