PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

An Overview on the Shrinkage Properties of Partial Least Squares Regression
Nicole Krämer
Computational Statistics Volume 22, Number 2, pp. 249-273, 2007.

Abstract

The aim of this paper is twofold. In the first part, we recapitulate the main results regarding the shrinkage properties of partial least squares (PLS) regression. In particular, we give an alternative proof of the shape of the PLS shrinkage factors. It is well known that some of the factors are >1. We discuss in detail the effect of shrinkage factors for the mean squared error of linear estimators and argue that we cannot extend the results to PLS directly, as it is nonlinear. In the second part, we investigate the effect of shrinkage factors empirically. In particular, we point out that experiments on simulated and real world data show that bounding the absolute value of the PLS shrinkage factors by 1 seems to leads to a lower mean squared error.

EPrint Type:Article
Additional Information:A preprint can be downloaded from http://ml.cs.tu-berlin.de/~nkraemer/publications.html
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3039
Deposited By:Nicole Krämer
Deposited On:16 September 2007