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Title: Selection of variables in Discrete Discriminant Analysis
Authors: Marques, A.
Ferreira, A. S.
Cardoso, M. G. M. S.
Keywords: Combining models
Discrete Discriminant Analysis
Variable selection
Issue Date: 2013
Publisher: Walter de Gruyter GmbH
Abstract: In Discrete Discriminant Analysis one often has to deal with dimensionality problems. In fact, even a moderate number of explanatory variables leads to an enormous number of possible states (outcomes) when compared to the number of objects under study, as occurs particularly in the social sciences, humanities and health-related elds. As a consequence, classi cation or discriminant models may exhibit poor performance due to the large number of parameters to be estimated. In the present paper, we discuss variable selection techniques which aim to address the issue of dimensionality. We speci cally perform classi cation using a combined model approach. In this setting, variable selection is particularly pertinent, enabling the handling of degrees of freedom and reducing computational cost.
Peer reviewed: yes
DOI: 10.2478/bile-2013-0013
ISSN: 1896-3811
Publisher version: The definitive version is available at:
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica

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