Partial least-squares vs. Lanczos bidiagonalization—I: analysis of a projection method for multiple regression-Reference-Cited by-同舟云学术

Partial least-squares vs. Lanczos bidiagonalization—I: analysis of a projection method for multiple regression

Published:2004-05 Issue:1 Volume:46 Page:11-31
ISSN:0167-9473
Container-title:Computational Statistics & Data Analysis
language:en
Short-container-title:Computational Statistics & Data Analysis

Author:

Eldén Lars

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computational Mathematics,Statistics and Probability

Reference28 articles.

1. Adolfsson, T., 1999. Partial least-squares and its implementations from a statistical point of view. Lic. Thesis, Chalmers Univ. of Technology, Studies in Applied Probability and Statistics.

2. Iterative Solution Methods;Axelsson,1994

3. Numerical Methods for Least Squares Problems;Björck,1996

4. Stability of conjugate gradient and Lanczos methods for linear least-squares problems;Björck;SIAM J. Matrix Anal. Appl.,1998

5. Frameworks for latent variable multivariate regression;Burnham;J. Chemometrics,1996

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