The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Partial least squares algorithms and methods
Abstract
Partial least squares (PLS) refers to a set of iterative algorithms based on least squares that implement a broad spectrum of both explanatory and exploratory multivariate techniques, from regression to path modeling, and from principal component to multi‐block data analysis. This article focuses on PLS regression and PLS path modeling, which are PLS approaches to regularized regression and to predictive path modeling. The computational flows and the optimization criteria of these methods are reviewed in detail, as well as the tools for the assessment and interpretation of PLS models. The most recent developments and some of the most promising on going researches are enhanced.WIREs Comput Stat 2013, 5:1–19. doi: 10.1002/wics.1239 This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Exploratory Data Analysis Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical Models > Linear Models Algorithms and Computational Methods > Least Squares
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PLS-regression: a basic tool of chemometrics
Wold, S., Sjöström, M., & Eriksson, L. (2001) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/s0169-7439(01)00155-1 · Cited by 10,084
Ridge regression: Biased estimation for nonorthogonal problems
Hoerl, A. E., & Kennard, R. W. (1970) · Technometrics · doi:10.1080/00401706.1970.10488634 · Cited by 9,648
The Jackknife, the Bootstrap and Other Resampling Plans
Efron, B. (1982) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970319 · Cited by 8,120
Wold, S., Ruhe, A., Wold, H., & Dunn, III (1984) · SIAM Journal on Scientific and Statistical Computing · doi:10.1137/0905052 · Cited by 2,529
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Engineering of inorganic nanoparticles as magnetic resonance imaging contrast agents
Ni, D., Bu, W., Ehlerding, E. B., Cai, W., & Shi, J. (2017) · Chemical Society Reviews · doi:10.1039/c7cs00316a · Cited by 484
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Multiset sparse partial least squares path modeling for high dimensional omics data analysis
Csala, A., Zwinderman, A. H., & Hof, M. H. (2020) · BMC Bioinformatics · doi:10.1186/s12859-019-3286-3 · Cited by 26
“The descriptions of the objective functions of PLS-PM can be found in [22, 24] and the objective function for msPLS is given by Eq.”
BackgroundMethodInfluential
Metadata
- Authors
- Esposito Vinzi, V., & Russolillo, G.
- Venue
- WIREs Computational Statistics
- Publisher
- Wiley
- Published
- 2012-12-26
- Volume
- 5
- Issue
- 1
- Pages
- 1-19
- ISSN
- 1939-0068 (ISSN-L)
- 1939-5108
- OpenAlex
- W2099410559
- Cited by
- 180
- References
- 4
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- closed
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