Multiblock Method for Categorical Variables. Application to the Study of Antibiotic Resistance

Stéphanie Bougeard (), El Mostafa Qannari () and Claire Chauvin ()
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Stéphanie Bougeard: AFSSA (French Agency for Food Safety), Department of Epidemiology
El Mostafa Qannari: ONIRIS (Nantes-Atlantic National College of Veterinary Medicine, Food Science and Engineering), Department of Sensometrics and Chemometrics
Claire Chauvin: AFSSA (French Agency for Food Safety), Department of Epidemiology

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 389-396 from Springer

Abstract: Abstract We address the problem of describing several categorical variables with a prediction purpose. We focus on methods in the multiblock modelling framework, each block being formed of the indicator matrix associated with each qualitative variable.We propose a method, called categorical multiblock Redundancy Analysis, based on a well-identified global optimization criterion which leads to an eigensolution. In comparison with usual procedures, such as logistic regression, the method is well-adapted to the case of a large number of redundant explanatory variables. Practical uses of the proposed method are illustrated using an empirical example in the field of epidemiology.

Keywords: supervised classification; discriminant analysis; multiblock redundancy analysis; multiblock PLS; categorical variables (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_36

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DOI: 10.1007/978-3-7908-2604-3_36

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