Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
416730 | Computational Statistics & Data Analysis | 2006 | 23 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
John E. Hinkle, William S. Rayens,