Title: Partial Least Squares: A Framework
for Data Analysis and Statistical Modeling
Description:
PLS (Partial Least Squares) Regression is a
statistical modeling technique with data analysis features linking a block of
response variables to a block of explanatory variables. This method is feasible
even in critical situations often encountered in real practice when, for
instance, there are missing data or too few observations or too many variables
or a too strong correlation between the explanatory variables. The principle of
the PLS algorithm may be similarly used in order to yield extensions of PLS
regression to the case of logistic regression or, more generally, to PLS
generalised linear regression. Yet, PLS regression is only one of the methods
within the more general PLS framework. PLS regression may be actually meant as a
specific case of the PLS approach to structural equation modeling when only two
blocks of variables are available. This general approach studies a system of
linear relationships between latent (non observable) variables. Each latent
variable is described by a set of manifest (observable) indicators. The nature
of this approach is rather exploratory and data-driven than confirmatory. It is
usually referred to as a "soft" modeling approach as no normality hypothesis is
required, there is no constraint on the measurement scale of the manifest
indicators and the number of observations may be limited with respect to the
number of variables. The features of PLS-based methods make them very
interesting for further methodological developments and for applications in
several domains. The PLS track will provide an expository review of the
methodologies comprised in the PLS framework with a specific focus on the
critical assessment of the most recent developments with new statistical and
computational topics, the presentation of fresh ideas on the PLS theory and
methodology, the comparison of these methods with other competitors or
alternatives, and the estimation of the future directions of research in the
field.
Focus:
PLS Regression and related extensions
PLS Path Modeling and Alternative Approaches
PLS vs. Methods for Multiblock Analysis
Missing Data treatment in PLS
Classification issues in PLS
New Validation and Interpretation tools for PLS
Outlier Identification in PLS
PLS for Stochastic Processes
PLS with very large datasets
PLS for complex (L, U, Domino, Networks) data structures
Co-Chairs:
Carlo Lauro
Dipartimento di Matematica e Statistica
Facolta di Economia
Universita degli Studi di Napoli "Federico II"
Via Cintia, 26 - Complesso Monte SantAngelo
80126 Napoli - Italy
E-mail: carlo.lauro@unina.it
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Vincenzo Esposito Vinzi
Dipartimento di Matematica e Statistica
Facolta di Economia
Universita degli Studi di Napoli "Federico II"
Via Cintia, 26 - Complesso Monte SantAngelo
80126 Napoli - Italy
E-mail: vincenzo.espositovinzi@unina.it |
Wynne W. Chin
Bauer Faculty Fellow
Department of Decision and Information Sciences
C.T. Bauer College of Business
334 Melcher Hall, room 280D
University of Houston
Houston, Texas 77204-6282 USA
E-mail: wchin@uh.edu |
Aloke Phatak
CSIRO Mathematical and Information Sciences
Private Bag No. 5
Wembley, WA 6913
Australia
E-mail: Aloke.Phatak@csiro.au
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