Performance of some variable selection methods when multicollinearity is present

By Il-Gyo Chong, Chi-Hyuck Jun. 2005

1,987 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/772c8c016d60089ba42d94c223527e87a8e7c4ee

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

1. Da‐Wen Sun
2. Xin‐an Zeng
3. Dan Liu
4. Fei Liu
5. Chu Zhang
6. Hao Jiang
7. Alejandro C. Olivieri
8. Yong He

In Control and Systems Engineering

1. R. Teófilo
2. Santiago A. Bortolato
3. E. Arancibia
4. Alejandro C. Olivieri
5. Rasmus Bro
6. Márcia M. C. Ferreira
7. U. Shapira
8. Natalia Sorol

In Statistics and Probability

1. Da‐Wen Sun
2. Alejandro C. Olivieri
3. R. Teófilo
4. Santiago A. Bortolato
5. E. Arancibia
6. Solve Sæbø
7. Rasmus Bro
8. Márcia M. C. Ferreira

Citations in the corpus

• Variable selection in multi-block regression (2016, methodology): “There are many methods for variable selection in general and for PLS in particular [15-18, 20-26].”
• Ground-level hyperspectral imagery for detecting weeds in wheat fields (2013, background): “The VIP is defined as the summary of the importance for each predictor projections to find a number of principal components of the PLS model (Chong and Jun 2005; Cohen et al. 2010).”
• Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry (2014, methodology): “The first one is to use variable importance in projection (VIP) scores (PLS-VIP method), and the other is to use regression coefficients estimated by PLS regression (PLS-β method) (Chong and Jun 2005).”