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E-raamat: Handbook of Partial Least Squares: Concepts, Methods and Applications

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Partial Least Squares is a family of regression based methods designed for the an- ysis of high dimensional data in a low-structure environment. Its origin lies in the sixties, seventies and eighties of the previous century, when Herman O. A. Wold vigorously pursued the creation and construction of models and methods for the social sciences, where soft models and soft data were the rule rather than the exception, and where approaches strongly oriented at prediction would be of great value. Theauthorwasfortunatetowitnessthedevelopment rsthandforafewyears. Herman Wold suggested (in 1977) to write a PhD-thesis on LISREL versus PLS in the context of latent variable models, more speci cally of the basic design. I was invited to his research team at the Wharton School, Philadelphia, in the fall of 1977. Herman Wold also honoured me by serving on my PhD-committee as a distinguished and decisive member. The thesis was nished in 1981. While I moved into another direction (speci cation, estimation and statistical inference in the c- text of model uncertainty) PLS sprouted very fruitfully in many directions, not only as regards theoretical extensions and innovations (multilevel, nonlinear extensions et cetera) but also as regards applications, notably in chemometrics, marketing, and political sciences. The PLS regression oriented methodology became part of main stream statistical analysis, as can be gathered from references and discussions in important books and journals. See e. g. Hastie et al. (2001), or Stone and Brooks (1990),Frank and Friedman (1993),Tenenhauset al. (2005),there are manyothers.

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From the reviews:

I found the book to be a good resource for those without prior knowledge of PLS looking forward to an introduction as well as a comprehensive reference for every researcher and every practitioner interested in the most recent advances in PLS methodology. All of them should have this title as an essential part of their library. Great Stuff! will be the definitive reference work in the field for a good time to come. it is wonderfully readable and referable. (Current Engineering Practice, August, 2011)

The book is divided into three parts, dealing, respectively, with contemporary methodological developments, applications in marketing and related areas, and tutorials on particular aspects of PLS analysis. It provides a comprehensive overview of recent advances in the area, and describes cutting-edge methodological developments showing how PLS can be applied in tackling a wide variety of problem types. for people who do have some prior experience with PLS, this book is a goldmine of novel methodological and practical applications. (David J. Hand, International Statistical Review, Vol. 80 (3), 2012)

Editorial: Perspectives on Partial Least Squares.- METHODS.- Latent
Variables and Indices: Herman Wold#x2019;s Basic Design and Partial Least
Squares.- PLS Path Modeling: From Foundations to Recent Developments and Open
Issues for Model Assessment and Improvement.- Bootstrap Cross-Validation
Indices for PLS Path Model Assessment.- A Bridge Between PLS Path Modeling
and Multi-Block Data Analysis.- Use of ULS-SEM and PLS-SEM to Measure a Group
Effect in a Regression Model Relating Two Blocks of Binary Variables.- A New
Multiblock PLS Based Method to Estimate Causal Models: Application to the
Post-Consumption Behavior in Tourism.- An Introduction to a Permutation Based
Procedure for Multi-Group PLS Analysis: Results of Tests of Differences on
Simulated Data and a Cross Cultural Analysis of the Sourcing of Information
System Services Between Germany and the USA.- Finite Mixture Partial Least
Squares Analysis: Methodology and Numerical Examples.- Prediction Oriented
Classification in PLS Path Modeling.- Conjoint Use of Variables Clustering
and PLS Structural Equations Modeling.- Design of PLS-Based Satisfaction
Studies.- A Case Study of a Customer Satisfaction Problem: Bootstrap and
Imputation Techniques.- Comparison of Likelihood and PLS Estimators for
Structural Equation Modeling: A Simulation with Customer Satisfaction Data.-
Modeling Customer Satisfaction: A Comparative Performance Evaluation of
Covariance Structure Analysis Versus Partial Least Squares.- PLS in Data
Mining and Data Integration.- Three-Block Data Modeling by Endo- and Exo-LPLS
Regression.- Regression Modelling Analysis on Compositional Data.-
APPLICATIONS TO MARKETING AND RELATED AREAS.- PLS and Success Factor Studies
in Marketing.- Applying Maximum Likelihood and PLS on Different Sample Sizes:
Studieson SERVQUAL Model and Employee Behavior Model.- A PLS Model to Study
Brand Preference: An Application to the Mobile Phone Market.- An Application
of PLS in Multi-Group Analysis: The Need for Differentiated Corporate-Level
Marketing in the Mobile Communications Industry.- Modeling the Impact of
Corporate Reputation on Customer Satisfaction and Loyalty Using Partial Least
Squares.- Reframing Customer Value in a Service-Based Paradigm: An Evaluation
of a Formative Measure in a Multi-industry, Cross-cultural Context.-
Analyzing Factorial Data Using PLS: Application in an Online Complaining
Context.- Application of PLS in Marketing: Content Strategies on the
Internet.- Use of Partial Least Squares (PLS) in TQM Research: TQM Practices
and Business Performance in SMEs.- Using PLS to Investigate Interaction
Effects Between Higher Order Branding Constructs.- TUTORIALS.- How to Write
Up and Report PLS Analyses.- Evaluation of Structural Equation Models Using
the Partial Least Squares (PLS) Approach.- Testing Moderating Effects in PLS
Path Models: An Illustration of Available Procedures.- A Comparison of
Current PLS Path Modeling Software: Features, Ease-of-Use, and Performance.-
to SIMCA-P and Its Application.- Interpretation of the Preferences of
Automotive Customers Applied to Air Conditioning Supports by Combining GPA
and PLS Regression.
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