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On Variance Estimator of Partial Least Squares Regression
Linköping University, Department of Mathematics. Linköping University, The Institute of Technology.
Department of Energy and Technology, Swedish University of Agriculture Sciences, Uppsala.
2010 (English)Report (Other academic)
Abstract [en]

Univariate partial least squares regression (PLS1) is a method of modeling

relationships between a response variable and explanatory variables,

especially when the explanatory variables are almost collinear.

The purpose is to predict a future response observation, although in

many applications there is an interest to understand the contributions

of each explanatory variable. It is an algorithmic approach and in the

paper we are going to use the algorithm presented by Helland (1988).

The population PLS predictor is linked to a linear model including

a Krylov design matrix and a two step estimation procedure. For the

rst step the maximum likelihood approach will be applied to a speci c

multivariate linear model, generating tools for evaluating the information

in the explanatory variables. It is shown that explicit maximum

likelihood estimators of the dispersion matrix can be obtained where

the dispersion matrix, besides representing the variation in the error,

also is included in a Krylov structured design matrix describing the


Place, publisher, year, edition, pages
Linköping, 2010. , 11 p.
Keyword [en]
MLE; Krylov Design Matrix; PLS; Variance Estimator.
National Category
Probability Theory and Statistics
URN: urn:nbn:se:liu:diva-63054Local ID: LiTH-MAT-R--2010/10--SEOAI: diva2:381926
Available from: 2010-12-29 Created: 2010-12-09 Last updated: 2010-12-29

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