Latent Variable Path Modeling with Partial Least Squares

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Partial Least Squares (PLS) is an estimation method and an algorithm for latent variable path (LVP) models. PLS is a component technique and estimates the latent variables as weighted aggregates. The implications of this choice are considered and compared to covariance structure techniques like LISREL, COSAN and EQS. The properties of special cases of PLS (regression, factor scores, structural equations, principal components, canonical correlation, hierarchical components, correspondence analysis, three-mode path and component analysis) are examined step by step and contribute to the understanding of the general PLS technique. The proof of the convergence of the PLS algorithm is extended beyond two-block models. Some 10 computer programs and 100 applications of PLS are referenced. The book gives the statistical underpinning for the computer programs PLS 1.8, which is in use in some 100 university computer centers, and for PLS/PC. It is intended to be the background reference for the users of PLS 1.8, not as textbook or program manual.           


Series:Economic Theory
Author: Lohmöller, Jan-Bernd
Hardcover:284pages
Publisher:Springer
Language: English
ISBN: 9783642525124

Contents
1 .Basic  Principles  of  Model  Building
2.The  Basic  and  the  Extended  PLS  Method
3 . Foundations  of  Partial  Least  Squares
4 . Mixed  Measurement  Level  Multivariate  Data
5 . Predictive  vs.  Structural  Modeling:  PLS  vs.  ML
6 . Latent  Variables  Three-Mode  Path  (LVP3)  Analysis
7 .PLS  Programs  and  Applications  

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关键词：Variable Modeling Partial Squares Latent techniques principal structure special choice

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