The DOI matches its Crossref record.
A Note on the Use of Principal Components in Regression
Abstract
The use of principal components in regression has received a lot of attention in the literature in the past few years, and the topic is now beginning to appear in textbooks. Along with the use of principal component regression there appears to have been a growth in the misconception that the principal components with small eigenvalues will very rarely be of any use in regression. The purpose of this note is to demonstrate that these components can be as important as those with large variance. This is illustrated with four examples, three of which have already appeared in the literature.
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
Cited by
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922
“variable on the explanatory variables directly, the principal components of the explanatory variables which are extracted by the PCA method are used as regressors [129].”
MethodKernel partial least squares regression in reproducing kernel hilbert space
Rosipal, R., & Trejo, L. J. (2002) · doi:10.5555/944790.944806 · Cited by 873
Regression, Prediction and Shrinkage
Copas, J. B. (1983) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1983.tb01258.x · Cited by 612
Higgins-Chen, A. T., Thrush, K. L., Wang, Y., Minteer, C. J., Kuo, P.-L., Wang, M., Niimi, P., Sturm, G., Lin, J., Moore, A. Z., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00248-2 · Cited by 502
Digital medicine and the curse of dimensionality
Berisha, V., Krantsevich, C., Hahn, P. R., Hahn, S., Dasarathy, G., Turaga, P., & Liss, J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00521-5 · Cited by 344
Metadata
- Authors
- Jolliffe, I. T.
- Venue
- Applied Statistics
- Publisher
- JSTOR
- Published
- 1982
- Volume
- 31
- Issue
- 3
- Pages
- 300
- ISSN
- 0035-9254 (ISSN-L)
- OpenAlex
- W2740586183
- Cited by
- 829
- References
- 0
- FWCI
- 5.21
- 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: A Note on the Use of Principal Components in Regression. https://app.cochat.ai/papers/10.2307/2348005
- The paper
- Jolliffe, I. T. (1982). A Note on the Use of Principal Components in Regression. Applied Statistics. https://doi.org/10.2307/2348005
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.