On the use of Structural Equation Models and PLS Path Modeling to build composite indicators

Nowadays there is a pre-eminent need to measure very complex phenomena like poverty, progress, well-being, etc. As is well known, the main feature of a composite indicator is that it summarizes complex and multidimensional issues. Thanks to its features, Structural Equation Modeling seems to be a useful tool for building systems of composite indicators. Among the several methods that have been developed to estimate Structural Equation Models we focus on the PLS Path Modeling approach (PLS-PM), because of the key role that estimation of the latent variables (i.e. the composite indicators) plays in the estimation process. In this work, first we present Structural Equation Models and PLS-PM. Then we provide a suite of statistical methodologies for handling categorical indicators in PLS-PM. In particular, in order to take categorical indicators into account, we propose to use a modified version of the PLS-PM algorithm recently presented by Russolillo [2009]. This new approach provides a quantification of the categorical indicators in such a way that the weight of each quantified indicator is coherent with the explicative ability of the corresponding categorical indicator. To conclude, an application involving data taken from a paper by Russet [1964] will be presented.PLS Path Modeling,Categorical Indicators,Structural Equation Modeling,Composite Indicators

Capturing and Treating Unobserved Heterogeneity by Response Based Segmentation in PLS Path Modeling. A Comparison of Alternative Methods by Computational Experiments

Segmentation in PLS path modeling framework results is a critical issue in social sciences. The assumption that data is collected from a single homogeneous population is often unrealistic. Sequential clustering techniques on the manifest variables level are ineffective to account for heterogeneity in path model estimates. Three PLS path model related statistical approaches have been developed as solutions for this problem. The purpose of this paper is to present a study on sets of simulated data with different characteristics that allows a primary assessment of these methodologies.Partial Least Squares; Path Modeling; Unobserved Heterogeneity

Variable Selection in Partial Least Squares Methods: overview and recent developments

Recent developments in technology enable collecting a large amount of data from various sources. Moreover, many real world applications require studying relations among several groups of variables. The analysis of landscape matrices, i.e. matrices having more columns (variables, p) than rows (observations, n), is a challenging task in several domains. Two different kinds of problems arise when dealing with high dimensional data sets characterized by landscape matrices. The first refers to computational and numerical problems. The second deals with the difficulty in assessing and understanding the results. Dimension reduction seems to be a solution to solve both problems. We should distinguish between feature selection and feature extraction. The first refers to variable selection, while feature extraction aims to transform the data from high-dimensional space to low-dimensional space. Partial Least Squares (PLS) methods are classical feature extraction tools that work in the case of high-dimensional data sets. Since PLS methods do not require matrices inversion or diagonalization, they allow us to solve computational problems. However, results interpretation is still a hard problem when facing with very high-dimensional data sets. Moreover, recently Chun & Keles (2010) showed that asymptotic consistency of PLS regression estimator for the univariate case does not hold with the very large p and small n paradigm. Nowadays interest is increasing in developing new PLS methods able to be, at the same time, a feature extraction tool and a feature selection method. The first attempt to perform variable selection in univariate PLS Regression framework was presented by Bastien et al. in 2005. More recently Le Cao et al. (2008) and Chun & Keles (2010) proposed two different approaches to include variable selection in PLS Regression, based on L1 penalization (Tibshirani, 1996). In our work, we will investigate all these approaches and discuss the pros and cons. Moreover, a new version of PLS Path Modeling algorithm including variable selection will be presented

Lithiation of 4-membered heterocycles as useful strategy for the preparation of new molecular scaffolds: addressing the regioselectivity in azetidines and thietanes

Four-membered heterocycles (4-MH) with one or two heteroatoms are of great importance in medicinal chemistry and synthetic organic chemistry. This kind of scaffolds show peculiar structural features, related to the ring “puckering”, and biological properties. Our recent research efforts have been focused on the stereoselective synthesis and functionalization of some 4-MH such as azetidines, thietanes and oxazetidines

Global Criteria for Sparse Penalized Partial Least Squares

http://www.statistics.gov.hk/wsc/STS068-P4-A.pdfInternational audienc

Dynamics of the Fermentation Process and Chemical Profiling of Pomegranate (Punica granatum L.) Wines Obtained by Different Cultivar×Yeast Combinations

Pomegranate (Punica granatum L.) is one of the historical tree crops in the Mediterranean region and is nowadays commercialized for its beneficial properties in the form of fruits, juice, jams and, in some East countries, as fermented juice (pomegranate wine). However, pomegranate wines are not established as a common beverage in Western countries. In this work, we produced pomegranate wines using two cultivars and two yeasts (Saccharomyces cerevisiae strain Clos and S. cerevisiae ex-bayanus strain EC1118) with contrasting characteristics. A comprehensive chemical profile of the wines was obtained. Notable differences were observed in the function of the cultivars and the yeasts. Different cultivar×yeast combinations provided wines with clearly different chemical profiles and specific features in the patterns of organic acids, phenolics, and volatile compounds. This highlights the opportunity to obtain tailored pomegranate wines with desired chemical profiles and, consequently, sensory properties, through management optimization of pomegranate winemaking. In this view, pomegranate wines have the potential to become an established beverage in Western countries

Weed Functional Diversity as Affected by Agroecological Service Crops and No-Till in a Mediterranean Organic Vegetable System

This paper explores the effect of agroecological service crops (ASCs), i.e., crops included in the crop rotation for their ecosystem services, terminated with an in-line tillage roller crimper (ILRC) on weed community composition and their functional traits in comparison to a tilled control without ASC. A two-year study was performed in a long-term experiment with vegetables under organic management. Four different cereal crops were introduced as ASCs. Weed abundance and richness and the functional traits were assessed at three different stages, i.e., before and after ASC termination and before harvest of the following crop, melon. All the ASCs showed strong weed suppression, with few differences between the cereals tested. Weed communities with ASCs had later flowering onset and wider flowering span compared to the control, which positively affects weed dispersal and attraction of beneficial insects. However, weed communities with ASCs had higher values for traits related to competition (specific leaf area, seed weight and more perennials). A trade-off between weed suppression and selection of more competitive weed communities by the introduction of ASCs managed with the ILRC should be evaluated in the long-run. The use of the ILRC alternating with other soil management practices seems the more effective strategy to benefit from the minimal soil tillage while avoiding the selection of disservice-related traits in the weed community

An integrated PLS Regression-based approach for multidimensional blocks in PLS path modeling

International audienceL'approche PLS aux modèles à équations structurelles (PLS Path Modeling, PLS-PM) est couramment considérée comme une approche basée sur les composantes. Cette méthode a été récemment revisitée en tant que cadre général pour l'analyse des tableaux multiples. Nous proposons ici deux nouvelles méthodes d'estimation des poids externes dans le cadre de la PLS-PM: le Mode PLScore et le Mode PLScow. Chaque mode est fondé sur l'utilisation de la régression PLS pour l'étape d'estimation externe. Toutefois, en Mode PLScore une régression PLS est exécutée sous les contraintes classiques de la PLS-PM de variance unitaire pour les scores des variables latentes ; tandis que dans le Mode PLScow les poids externes sont contraints d'avoir une norme unitaire. Cette dernière contrainte est la contrainte classique de normalisation dans le cadre de la régression PLS. Nous montrons comment les deux nouveaux modes sont liés aux méthodes d'estimation externe classiques de la PLS-PM, c.-à-d. au Mode A et au Mode B, ainsi qu'au Nouveau Mode A récemment proposé par Tenenhaus & Tenenhaus (2009)