Ludovic Lebart

Bio: Ludovic Lebart is an academic researcher from Télécom ParisTech. The author has contributed to research in topics: Correspondence analysis & Multiple correspondence analysis. The author has an hindex of 19, co-authored 52 publications receiving 2720 citations. Previous affiliations of Ludovic Lebart include École Normale Supérieure & Centre national de la recherche scientifique.

Statistique exploratoire multidimensionnelle

Abstract: Ouvragedestine aux etudiants de 2e cycle Sommaire: METHODES FACTORIELLES: Analyse generale, decomposition aux valeurs singulieres; Analyse en Composantes Principales; Analyse des correspondances; Analyse des correspondances multiples; QUELQUES METHODES DE CLASSIFICATION: Agregation autour des centres mobiles; Classification hierarchique; Classification mixte et description statistique des classes; Complementarite entre analyse factorielle et classificationLIENS AVEC LES METHODES EXPLICATIVES USUELLES, METHODES DERIVEES: Analyse canonique; Regression multiple, modele lineaire; Analyse factorielle discriminante; Modeles log-lineaires; Segmentation; Structures de graphe, analyses locales; Tableaux multiples, groupes de variables VALIDITE ET PORTEE DES RESULTATS: Signification des valeurs propres et des taux d'inertie; Stabilite des axes, des formes, des classes

Exploring Textual Data

Abstract: Researchers in a number of disciplines deal with large text sets requiring both text management and text analysis. Faced with a large amount of textual data collected in marketing surveys, literary investigations, historical archives and documentary data bases, these researchers require assistance with organizing, describing and comparing texts. Exploring Textual Data demonstrates how exploratory multivariate statistical methods such as correspondence analysis and cluster analysis can be used to help investigate, assimilate and evaluate textual data. The main text does not contain any strictly mathematical demonstrations, making it accessible to a large audience. This book is very user-friendly with proofs abstracted in the appendices. Full definitions of concepts, implementations of procedures and rules for reading and interpreting results are fully explored. A succession of examples is intended to allow the reader to appreciate the variety of actual and potential applications and the complementary processing methods. A glossary of terms is provided.

FactoMineR: An R Package for Multivariate Analysis

Abstract: In this article, we present FactoMineR an R package dedicated to multivariate data analysis. The main features of this package is the possibility to take into account different types of variables (quantitative or categorical), different types of structure on the data (a partition on the variables, a hierarchy on the variables, a partition on the individuals) and finally supplementary information (supplementary individuals and variables). Moreover, the dimensions issued from the different exploratory data analyses can be automatically described by quantitative and/or categorical variables. Numerous graphics are also available with various options. Finally, a graphical user interface is implemented within the Rcmdr environment in order to propose an user friendly package.

Principal component analysis

Abstract: Principal component analysis PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross-validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis CA in order to handle qualitative variables and as multiple factor analysis MFA in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semi-definite matrices and upon the singular value decomposition SVD of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc.

NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set

Abstract: Clustering is the partitioning of a set of objects into groups (clusters) so that objects within a group are more similar to each others than objects in different groups. Most of the clustering algorithms depend on some assumptions in order to define the subgroups present in a data set. As a consequence, the resulting clustering scheme requires some sort of evaluation as regards its validity. The evaluation procedure has to tackle difficult problems such as the quality of clusters, the degree with which a clustering scheme fits a specific data set and the optimal number of clusters in a partitioning. In the literature, a wide variety of indices have been proposed to find the optimal number of clusters in a partitioning of a data set during the clustering process. However, for most of indices proposed in the literature, programs are unavailable to test these indices and compare them. The R package NbClust has been developed for that purpose. It provides 30 indices which determine the number of clusters in a data set and it offers also the best clustering scheme from different results to the user. In addition, it provides a function to perform k-means and hierarchical clustering with different distance measures and aggregation methods. Any combination of validation indices and clustering methods can be requested in a single function call. This enables the user to simultaneously evaluate several clustering schemes while varying the number of clusters, to help determining the most appropriate number of clusters for the data set of interest.

The Diversity of Modern Capitalism

Abstract: This book considers why institutional forms of modern capitalist economies differ internationally, and proposes a typology of capitalism based on the theory of institutional complementarity Different economic models are not simply characterized by different institutional forms, but also by particular patterns of interaction between complementary institutions which are the core characteristics of these models Institutions are not just simply devices which would be chosen by 'social engineers' in order to perform a function as efficiently as possible; they are the outcome of a political economy process Therefore, institutional change should be envisaged not as a move towards a hypothetical 'one best way', but as a result of socio-political compromises Based on a theory of institutions and comparative capitalism, the book proposes an analysis of the diversity of modern economies - from America to Korea - and identifies five different models: the market-based Anglo-Saxon model; Asian capitalism; the Continental European model; the social democratic economies; and the Mediterranean model Each of these types of capitalism is characterized by specific institutional complementarities The question of the stability of the Continental European model of capitalism has been open since the beginning of the 1990s: inferior macroeconomic performance compared to Anglo-Saxon economies, alleged unsustainability of its welfare systems, too rigid markets, etc The book examines the institutional transformations that have taken place within Continental European economies and analyses the political project behind the attempts at transforming the Continental model It argues that Continental European economies will most likely stay very different from the market-based economies, and caat political strategies promoting institutional change aiming at convergence with the Anglo-Saxon model are bound to meet considerable opposition