The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis
Abstract
A random vector x arises from one of two multivariate normal distributions differing in mean but not covariance. A training set x 1, x 2, ··· x n of previous cases, along with their correct assignments, is known. These can be used to estimate Fisher's discriminant by maximum likelihood and then to assign x on the basis of the estimated discriminant, a method known as the normal discrimination procedure. Logistic regression does the same thing but with the estimation of Fisher's disriminant done conditionally on the observed values of x 1 x 2, ···, x n . This article computes the asymptotic relative efficiency of the two procedures. Typically, logistic regression is shown to be between one half and two thirds as effective as normal discrimination for statistically interesting values of the parameters.
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
Cites
An Introduction to Multivariate Statistical Analysis
Muirhead, R. J., & Anderson, T. W. (1986) · Journal of Business & Economic Statistics · doi:10.2307/1391399 · Cited by 9,082
Testing Statistical Hypotheses
(2021) · Wiley Series in Probability and Statistics · doi:10.1002/9781119243830.ch12 · Cited by 5,106
Introduction to Multivariate Statistical Analysis.
Madow, W. G., & Anderson, T. W. (1959) · The American Mathematical Monthly · doi:10.2307/2308777 · Cited by 3,878
(2008) · The Concise Encyclopedia of Statistics · doi:10.1007/978-0-387-32833-1_5 · Cited by 2,670
Introduction to Multivariate Statistical Analysis
Kendall, M. G., & Anderson, T. W. (1959) · Econometrica · doi:10.2307/1909376 · Cited by 1,339
Testing Statistical Hypotheses.
Stuart, A., & Lehmann, E. L. (1960) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2342657 · Cited by 616
Halperin, M., Blackwelder, W. C., & Verter, J. I. (1971) · Journal of Chronic Diseases · doi:10.1016/0021-9681(71)90106-8 · Cited by 242
Cited by
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229
Self-citationAn Introduction to Logistic Regression Analysis and Reporting
Peng, C.-Y. J., Lee, K. L., & Ingersoll, G. M. (2002) · The Journal of Educational Research · doi:10.1080/00220670209598786 · Cited by 2,671
Estimating the error rate of a prediction rule: improvement on cross-validation
Efron, B. (1983) · Journal of the American Statistical Association · doi:10.1080/01621459.1983.10477973 · Cited by 2,300
Self-citation(2001) · Cited by 1,932
Small sample size effects in statistical pattern recognition: Recommendations for practitioners
Raudys, S. J., & Jain, A. K. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.75512 · Cited by 1,384
Metadata
- Authors
- Efron, B.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1975-12
- Volume
- 70
- Issue
- 352
- Pages
- 892-898
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2090578816
- Cited by
- 567
- References
- 7
- FWCI
- 5.89
- 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: The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis. https://app.cochat.ai/papers/10.1080/01621459.1975.10480319
- The paper
- Efron, B. (1975). The Efficiency of Logistic Regression Compared to Normal Discriminant Analysis. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1975.10480319
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.
