The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Partial Least Squares Regression
Abstract
Partial least squares regression was introduced as an algorithm in the early 1980s, and it has gained much popularity in chemometrics. PLSR—or PLSR1—is a regression method for collinear data, and can be seen as a competitor to principal component regression. The method makes it possible to combine prediction with a study of a joint latent structure in the x,y‐variables. PLSR2 is a generalization to several dependent variables. PLSR1 can be understood from a statistical point of view via its parameter algorithm and the corresponding population model.
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Cites
Partial least-squares regression: a tutorial
Geladi, P., & Kowalski, B. R. (1986) · Analytica Chimica Acta · doi:10.1016/0003-2670(86)80028-9 · Cited by 6,992
Wold, S., Ruhe, A., Wold, H., & Dunn, III (1984) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0905052 · Cited by 2,529
A Statistical View of Some Chemometrics Regression Tools
Frank, L. E., & Friedman, J. H. (1993) · Technometrics · doi:10.1080/00401706.1993.10485033 · Cited by 2,213
SIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905
Höskuldsson, A. (1988) · Journal of Chemometrics · doi:10.1002/cem.1180020306 · Cited by 1,881
A fast non-negativity-constrained least squares algorithm
Bro, R., & de Jong, S. (1997) · Journal of Chemometrics · doi:10.1002/(sici)1099-128x(199709/10)11:5<367::aid-cem481>3.0.co;2-i · Cited by 909
Multiway calibration. Multilinear PLS
Bro, R. (1996) · Journal of Chemometrics · doi:10.1002/(sici)1099-128x(199601)10:1<47::aid-cem400>3.0.co;2-c · Cited by 714
Martens, H., & Næs, T. (1984) · Chemometrics · doi:10.1007/978-94-017-1026-8_5 · Cited by 597
On the structure of partial least squares regression
Helland, I. S. (1988) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918808812681 · Cited by 559
Self-citationAn Interpretation of Partial Least Squares
Garthwaite, P. H. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476452 · Cited by 556
Research works printed but not linked: 14
- 10.1111/1467-9868.00252 · doi:10.1111/1467-9868.00252
- 10.1002/cem.1180090406 · doi:10.1002/cem.1180090406
- 10.1214/aos/1032894467 · doi:10.1214/aos/1032894467
- Helland I. S., 2001, Algebraic Methods in Statistics
- 10.1016/S0169-7439(03)00087-X · doi:10.1016/s0169-7439(03)00087-x
- Helland I. S., 1994, Comparison of prediction methods when only a few components are relevant, J. Am. Stat. Assoc, 89, 583, 10.1080/01621459.1994.10476783 · doi:10.1080/01621459.1994.10476783
- 10.1002/(SICI)1099-128X(199707)11:4<283::AID-CEM475>3.0.CO;2-3 · doi:10.1002/(sici)1099-128x(199707)11:4<283::aid-cem475>3.0.co;2-3
- Martens H., 1989, Multivariate Calibration
- Tenenhaus M., 1998, La Regression PLS. Théorie et Pratique
- Tenenhaus M., 1999, L'approche PLS, Rev. Stat. Appl, 47, 5
- 10.1007/BF00142661 · doi:10.1007/bf00142661
- 10.1080/01621459.1997.10473676 · doi:10.1080/01621459.1997.10473676
- Næs T., 1993, Relevant components in regression, Scand. J. Stat, 20, 239
- von Rosen D., 1994, PLS, Linear models and invariant spaces, Scand. J. Stat, 21, 179
Cited by
Partial Least Squares Methods: Partial Least Squares Correlation and Partial Least Square Regression
Abdi, H., & Williams, L. J. (2013) · Methods in Molecular Biology · doi:10.1007/978-1-62703-059-5_23 · Cited by 448
A lightweight supervised intrusion detection mechanism for IoT networks
Roy, S., Li, J., Choi, B.-J., & Bai, Y. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2021.09.027 · Cited by 192
Fan, Y., Lai, K., Rasco, B. A., & Huang, Y. (2015) · LWT - Food Science and Technology · doi:10.1016/j.lwt.2014.08.011 · Cited by 116
Prebiotically plausible mechanisms increase compositional diversity of nucleic acid sequences
Derr, J., Manapat, M. L., Rajamani, S., Leu, K., Xulvi-Brunet, R., Joseph, I., Nowak, M. A., & Chen, I. A. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks065 · Cited by 74
Metadata
- Authors
- Helland, I.
- Venue
- Encyclopedia of Statistical Sciences
- Publisher
- Wiley
- Published
- 2005-12
- OpenAlex
- W1582645007
- Cited by
- 259
- References
- 28
- Type
- reference entry
- Language
- en
- Open access
- closed
- Licence
- doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
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