Some theoretical aspects of partial least squares regression

Chemometrics and Intelligent Laboratory Systems

Volumn 58, Issue 2, 2001, Pages 97-107

  Helland, Inge S ^a  

^a UNIVERSITY OF OSLO   (Norway)

Author keywords

Biased regression methods; Continuum regression; PCR; PLS; PLS algorithm; PLSR; Population model; Prediction; Prediction error; Regression; Relevant components; Ridge regression; Shrinkage

Indexed keywords

ALGORITHM; ANALYTICAL ERROR; CHEMOMETRIC ANALYSIS; CONFERENCE PAPER; POPULATION MODEL; PREDICTION; PRIORITY JOURNAL; REGRESSION ANALYSIS; STATISTICAL MODEL; THEORETICAL STUDY;

EID: 0035965460     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-7439(01)00154-X     Document Type: Conference Paper

References (26)

1. Length modified ridge regression
2. A simulation study on comparison of prediction methods when only a few components are relevant
3. A generalized view on continuum regression
4. The peculiar shrinkage properties of partial least squares regression
5. PLS fits closer than PCR
6. PLS shrinks
7. A statistical view of some chemometrics regression tools
8. Partial least squares algorithm yields shrinkage estimators
9. On the structure of partial least squares regression
10. Partial least squares regression and statistical models
11. Maximum likelihood regression on relevant components
12. Model reduction for prediction in regression models
13. Statistical inference under a fixed symmetry group
14. Reduction in regression models under symmetry
15. Comparison of prediction methods when only a few components are relevant
16. PLS regression methods
17. Shrinkage structure of partial least squares
18. Relevant components in regression
19. Comparison of prediction methods for collinear data
20. Continuum regression: Cross-validated sequentially constructed prediction embracing ordinary least squares, partial least squares and principal component regression (with discussion)
21. Continuum regression and ridge regression
22. Partial least squares
23. The multivariate calibration problem in chemistry solved by the PLS method