Selection of variables in Discrete Discriminant Analysis

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,...

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Bibliographic Details
Published in:Biometrical letters 2013, Vol.50 (1), p.1-14
Main Authors: Marques, Anabela, Ana Sousa Ferreira, Cardoso, Margarida GMS
Format: Article
Language:English
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Summary: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.
ISSN:1896-3811
2199-577X
DOI:10.2478/bile-2013-0013