Carlo Lauro

PLS path modeling

A presentation of the Partial Least Squares approach to Structural Equation Modeling (or PLS Path Modeling) is given together with a discussion of its extensions. This approach is compared with the estimation of Structural Equation Modeling by means of maximum likelihood (SEM-ML). Notwithstanding, this approach still shows some weaknesses. In this respect, some new improvements are proposed. Furthermore, PLS path modeling can be used for analyzing multiple tables so as to be related to more classical data analysis methods used in this field. Finally, a complete treatment of a real example is shown through the available software.

PLS Typological Regression: Algorithmic, Classification and Validation Issues

Classification, within a PLS regression framework, is classically meant in the sense of the SIMCA methodology, i.e. as the assignment of statistical units to a-priori defined classes. As a matter of fact, PLS components are built with the double objective of describing the set of explanatory variables while predicting the set of response variables. Taking into account this objective, a classification algorithm is developed that allows to build typologies of statistical units whose different local PLS models have an intrinsic explanatory power higher than the initial global PLS model. The typology induced by the algorithm may undergo a non parametric validation procedure based on bootstrap. Finally, the definition of a compromise model is investigated.

Computational statistics or statistical computing, is that the question?

From an historical point of view, the birth of the conference activity in the area of interest may be dated in 1967 when the Interface Symposium of Computer Science and Statistics took place in Atlanta. The term Statistical Computing has been later adopted by the two major scientific associations in this area, i.e. the Statistical Computing section of the American Statistical Association founded in 1972, and the corresponding section of ISI known as International Association for Statistical Computing. The latter, founded in 1977, ineluded the previous European group known as COMPSTAT, which started this series of biannual scientific meetings with the first one being held in Vienna in 1974.

Quality of life in South African Black women with alopecia: a pilot study.

Alopecia has been shown to have a significant impact on quality of life (QoL), particularly in women. However, there are no data for African populations. This study was conducted to pilot an original questionnaire and a model‐based methodology to measure QoL and its determinants in a sample of South African Black women of African ancestry with alopecia.

Multivariate Total Quality Control

The major focus of the book is on using the methods suitable for an on-line and off-line process control both in the univariate and multivariate case. The authors do not only concentrate on the standard situation when the errors accompanying the observed process are normally distributed, but also describe in detail the more general situations that call for the use of the robust and non-parametric approaches. Within these approaches, the use of recent methods of the multivariate analysis in the total quality control is enhanced with particular reference to the customer satisfaction area, the monitoring of interval data and the comparison of patterns generated from multioccasion observations. The authors cover both pratical computational aspects of the problem and the necessary mathematical background, taking into account requirements of total quality control.

Path Directions Incoherence in PLS Path Modeling: A Prediction-Oriented Solution

PLS-PM presents some inconsistencies in terms of coherence with the direction of the relationships specified in the path diagram (i.e., the path directions). The PLS-PM iterative algorithm analyzes interdependence among blocks and misses to distinguish explicitly between dependent and explanatory blocks in the structural model. This inconsistency of PLS-PM is illustrated using the simple two-blocks model. For the case of more than two blocks of variables, it is necessary to have a close look at the different criteria optimized by PLS-PM to show this issue. In general, the role of latent variables in the structural model depends on the way the outer weights are calculated. A recently proposed method, called Non-Symmetrical Component-based Path Modeling, which is based on the optimization of a redundancy-related criterion in a multi-block framework, respects the direction of the relationships specified in the structural model. In order to assess the quality of the model, we provide a new goodness-of-fit index based on redundancy criterion and prediction capability. Furthermore, we provide a procedure to address the problem of multicollinearity within blocks of variables.

Predictive Path Modeling Through PLS and Other Component-Based Approaches: Methodological Issues and Performance Evaluation

This chapter deals with the predictive use of PLS-PM and related component-based methods in an attempt to contribute to the recent debates on the suitability of PLS-PM for predictive purposes. Appropriate measures and evaluation criteria for the assessment of models in terms of predictive ability are more and more desirable in PLS-PM. The performance of the models can be improved by choosing the appropriate parameter estimation procedure among the different existing ones or by making developments and modifications of the latter. A recent example of this type of work is the non-symmetrical approach for component-based path modeling, which leads to a new method, called non-symmetrical composite-based path modeling. In the composites construction stage, this new method explicitly takes into account the directions of the relationships in the inner model. Results are promising for this new method, especially in terms of predictive relevance.

Visualization and Analysis of Multiple Time Series by Beanplot PCA

Beanplot time series have been introduced by the authors as an aggregated data representation, in terms of peculiar symbolic data, for dealing with large temporal datasets. In the presence of multiple beanplot time series it can be very interesting for interpretative aims to find useful syntheses. Here we propose an extension, based on PCA, of the previous approach to multiple beanplot time series. We show the usefulness of our proposal in the context of the analysis of different financial markets.

Beanplot Data Analysis in a Temporal Framework

We propose in this work a new approach for modelling, forecasting and clustering beanplot financial time series. The beanplot time series like the histogram time series or the interval time series can be very useful to model the intra-period variability of the series. These types of new time series can be very useful with High Frequency financial data, data collected with often irregularly spaced observations.

Factorial Conjoint Analysis Based Methodologies

Aim of this paper is to underline the main contributions in the context of Factorial Conjoint Analysis. The integration of Conjoint Analysis with the exploratory tools of Multidimensional Data Analysis is the basis of different research strategies, proposed by the authors, combining the common estimation method with its geometrical representation. Here we present a systematic and unitary review of some of these methodologies by taking into account their contribution to several open ended problems.