Abstract
Partial least squares (PLS) is a modeling technique that has met with considerable success, particularly in the fields of chemometrics and psychometrics. In this paper, we extend linear PLS to a nonlinear version called "reciprocal curves." Reciprocal curves are smooth one-dimensional curves that pass through the center of the data, are co-consistent and share several optimality properties originally identified with PLS.
Original language | English |
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Pages (from-to) | 836-858 |
Number of pages | 23 |
Journal | Computational Statistics and Data Analysis |
Volume | 51 |
Issue number | 2 |
DOIs | |
State | Published - Nov 15 2006 |
Keywords
- Nonlinear models
- Partial least squares
- Principal curves
ASJC Scopus subject areas
- Statistics and Probability
- Computational Mathematics
- Computational Theory and Mathematics
- Applied Mathematics