Combinatorial Inference in Geometric Data Analysis - Le Roux, Brigitte; Bienaise, Solène; Durand, Jean-Luc
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Combinatorial Inference in Geometric Data Analysis gives an overview of multidimensional statistical inference methods applicable to clouds of points that make no assumption on the process of generating data or distributions, and that are not based on random modelling but on permutation procedures recasting in a combinatorial framework.

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Produktbeschreibung
Combinatorial Inference in Geometric Data Analysis gives an overview of multidimensional statistical inference methods applicable to clouds of points that make no assumption on the process of generating data or distributions, and that are not based on random modelling but on permutation procedures recasting in a combinatorial framework.
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Autorenporträt
Brigitte Le Roux is associate researcher at Laboratoire de Mathématiques Appliquées (MAP5/CNRS) of the Paris Descartes university and at the political research center of Sciences-Po Paris (CEVIPOF/CNRS). She completed her doctoral dissertation in applied mathematics at the Faculté des Sciences de Paris in 1970 that was supervised by Jean-Paul Benzécri. She has contributed to numerous theoretical research works and full scale empirical studies involving Geometric Data Analysis. She has authored and co-authored nine books, especially on Geometric Data Analysis (2004, Kluwer Academic Publishers) and Multiple Correspondence Analysis (2010, QASS series of Sage publications, n° 163). Solène Bienaise is data scientist at Coheris (company). She completed her doctoral dissertation in applied mathematics in 2013 at the Paris Dauphine University, under the direction of Pierre Cazes and Brigitte Le Roux. Jean-Luc Durand is associate professor at the Psychology department and researcher at LEEC (Laboratoire d'Ethologie Expérimentale et Comparée) of Paris 13 University. He completed his doctoral dissertation in Psychology at Paris Descartes University in 1989, supervised by Henry Rouanet. He teaches statistical methodology in psychology and ethology.