Robust methods for partial least squares regression
By Mia Hubert, K. V. Branden. 2003
273 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Applied Mathematics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/109bd0fb2e5520f399122e4d0f4e1aebb65f609a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
- Mia Hubert
- Peter J. Rousseeuw
- K. V. Branden
- Fang-Xiang Wu
- Alioune Ngom
- Yifeng Li
- L. M. Carrascal
- I. Galván
In Applied Mathematics
In Statistics and Probability
- Mia Hubert
- Peter J. Rousseeuw
- K. V. Branden
- Fang-Xiang Wu
- Alioune Ngom
- Yifeng Li
- L. M. Carrascal
- I. Galván
Citations in the corpus
- Increased sensitivity in neuroimaging analyses using robust regression (2005, background): “Although several groups have developed robust versions of multivariate techniques ( Hubert and Vanden Branden, 2003; Hubert and Verboven, 2003; Hubert et al., 2002), we focus here on the former, bmassively univariateQ approach.”
- ROBPCA: A New Approach to Robust Principal Component Analysis (2005, methodology): “…and Christmann 2003) In addition, using ROBPCA in robust principal components regression (Hubert and Verboven2003) and robust partial least squares (Hubert and Vanden Branden 2003) has been investigated The ROBPCA method thus opens a door to practical robust multivariate calibration and to the…”
- A Comparative Study On Some Methods For Handling Multicollinearity Problems (2006, unknown): “These are B̂p,q = Rp,kÂk,q β̂0 = ȳ − B̂′ q,px̄ Note that for a univariate response variable (q = 1), the parameter estimate B̂p,1 can be rewritten as the vector β̂ ([12], [14] and [17]).”
- A review on machine learning principles for multi-view biological data integration (2016, methodology): “The attraction of PLS-based integrative models is their capability of selecting co-varying features among multiple feature sets [120].”
- Partial least squares regression as an alternative to current regression methods used in ecology (2009, background): “The extracted factors account for successively lower proportions of original variance (Hubert and Branden 2003, Tobias 2003, Maestre 2004).”