Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 224

The DOI matches its Crossref record.

Least Squares Approximation of Matrices by Additive and Multiplicative Models

Gabriel, K. R.·1978·Journal of the Royal Statistical Society Series B: Statistical Methodology

Abstract

Summary: Reduced rank approximation of matrices by Householder–Young methods is shown to be equivalent to fitting of a bilinear (multiplicative) model and to projection onto an optimally chosen subspace. The relation to linear approximation is made evident. Least squares fitting of a mixed linear (additive) and bilinear (multiplicative) model is proved to be a two-stage process: (1) fit the linear part of the model, then take residuals, and (2) fit the bilinear part to the residuals. Extensions of this result are given as well as some results on distributions of residuals and tests against alternatives of given rank. Statistical applications are shown in principal component analysis, in biplot graphical display and in fitting additive or Mandel-type models to two-way tables.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1111/j.2517-6161.1978.tb01663.x

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.

A few free checks a day · papers already verified open instantly · results are public

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

No references could be resolved yet.

Research works printed but not linked: 32

  1. Bradu, 1974, Simultaneous statistical inference on interactions in two-way analysis of variance, J. Amer. Statist. Ass., 69, 428, 10.1080/01621459.1974.10482969 · doi:10.1080/01621459.1974.10482969
  2. Bradu, 1978, The biplot as a diagnostic tool for models in two-way tables, Technometrics, 20, 47, 10.1080/00401706.1978.10489617 · DOI · Paper page
  3. Corsten, 1972, Multiplicative effects in two-way analysis of variance, Statist. Neerlandica, 26, 61, 10.1111/j.1467-9574.1972.tb00173.x · doi:10.1111/j.1467-9574.1972.tb00173.x
  4. Corsten, 1976, Graphical exploration in comparing variance matrices, Biometrics, 32, 851, 10.2307/2529269 · doi:10.2307/2529269
  5. Daniel, 1976, Applications of Statistics to Industrial Experimentation., 10.1002/9780470316467 · DOI · Paper page
  6. Fisher, 1923, Studies in crop variation. II. The manurial response of different potato varieties, J. Agric. Sci., 13, 311, 10.1017/S0021859600003592 · DOI · Paper page
  7. Gabriel, 1971, The biplot—graphical display of matrices with applications to principal component analysis, Biometrika, 58, 453, 10.1093/biomet/58.3.453 · DOI · Paper page
  8. Gabriel, 1972, Analysis of meterological data by means of canonical decomposition and biplots, J. Appl. Meteor., 11, 1071, 10.1175/1520-0450(1972)011<1071:AOMDBM>2.0.CO;2 · doi:10.1175/1520-0450(1972)011<1071:aomdbm>2.0.co;2
  9. Gabriel, 1976, Graphische Darstellung von Matrizen durch das Biplot, EDV in Medizin und Biologie, 7, 1
  10. Gnanadesikan, 1972, Robust estimates, residuals and outlier detection with multiresponse data, Biometrics, 28, 81, 10.2307/2528963 · DOI · Paper page
  11. Gollob, 1968, A statistical model which combines features of factor analytic and analysis of variance techniques, Psychometrika, 33, 73, 10.1007/BF02289676 · DOI · Paper page
  12. Good, 1969, Some applications of the singular decomposition of a matrix, Technometrics, 11, 823, 10.1080/00401706.1969.10490741 · DOI
  13. Haber, 1975, The singular value decomposition of random matrices
  14. Hald, 1952, Statistical Theory with Engineering Applications.
  15. Householder, 1938, Matrix approximation and latent roots, Am. Math. Monthly, 45, 165, 10.1080/00029890.1938.11990787 · doi:10.1080/00029890.1938.11990787
  16. Johnson, 1972, An analysis of a two-way model with interaction and no replication, J. Amer. Statist. Ass., 67, 862, 10.1080/01621459.1972.10481307 · DOI · Paper page
  17. Krishnaiah, 1974, On the evaluation of some distributions that arise in simultaneous tests for the equality of the latent roots of the covariance matrix, J. Multiv. Anal., 4, 265, 10.1016/0047-259X(74)90033-5 · doi:10.1016/0047-259x(74)90033-5
  18. Mandel, 1961, Non-additivity in two-way analysis of variance, J. Amer. Statist. Ass., 56, 878, 10.1080/01621459.1961.10482132 · DOI · Paper page
  19. Mandel, 1969, The partitioning of interaction in analysis of variance, J. Res., National Bureau of Standards, Section B, Mathematical Sciences, 73B, 309, 10.6028/jres.073B.031 · DOI · Paper page
  20. Mandel, 1971, A new analysis of variance model for non-additive data, Technometrics, 13, 1, 10.1080/00401706.1971.10488751 · DOI · Paper page
  21. Okamoto, 1972, Four techniques of principal component analysis, J. Japan. Stat. Soc., 2, 63
  22. Pearson, 1901, On lines and planes of closest fit to systems of points in space, Phil. Mag., 2, 559, 10.1080/14786440109462720 · DOI · Paper page
  23. Rao, 1964, The use and interpretation of principal component analysis in applied research, Sankhyā A, 26, 329
  24. Rao, 1973, Linear Statistical Inference and its Application., 10.1002/9780470316436 · DOI · Paper page
  25. Scheffé, 1959, The Analysis of Variance.
  26. Schuurmann, 1973, On the distribution of the ratios of the extreme roots to the trace of the Wishart matrix, J. Multiv. Anal., 3, 445, 10.1016/0047-259X(73)90033-X · DOI
  27. Tukey, 1949, One degree of freedom for non-additivity, Biometrics, 5, 232, 10.2307/3001938 · DOI · Paper page
  28. Neumann, 1927, Some matrix inequalities and metrization of matrix space, Tomsk. Univ. Rev., 1, 286
  29. Williams, 1952, The interpretation of interactions in factorial experiments, Biometrika, 39, 65, 10.1093/biomet/39.1-2.65 · DOI
  30. Whittle, 1952, On principal components and least square methods of factor analysis, Skand. Aktuar., 35, 232
  31. Wold, 1966, Nonlinear estimation by iterative least squares procedures
  32. Yates, 1933, The analysis of replicated experiments when the field results are incomplete, Empire J. Exp. Agr., 1, 129

Cited by

See all 224

Metadata

Authors
Gabriel, K. R.
Venue
Journal of the Royal Statistical Society Series B: Statistical Methodology
Published
1978-01-01
Volume
40
Issue
2
Pages
186-196
ISSN
  • 1369-7412 (ISSN-L)
  • 1467-9868
OpenAlex
W131298435
Cited by
224
References
0
FWCI
2.48
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: Least Squares Approximation of Matrices by Additive and Multiplicative Models. https://app.cochat.ai/papers/10.1111/j.2517-6161.1978.tb01663.x
The paper
Gabriel, K. R. (1978). Least Squares Approximation of Matrices by Additive and Multiplicative Models. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/j.2517-6161.1978.tb01663.x

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.