<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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. </p></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.

Selection of variables in Discrete Discriminant Analysis

Barreiro College of Technology, Setúbal Polytechnic, IPS, Portugal

LEAD, Faculty of Psychology, University of Lisbon, Portugal, CEAUL and UNIDE

Dep. of Quantitative Methods of ISCTE - Lisbon University Institute, Portugal and UNIDE

Biometrical Letters

{"article-title":"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...

Selection of variables in Discrete Discriminant Analysis

Online veröffentlicht: 05. Juni 2013

Seitenbereich: 1 - 14

DOI: https://doi.org/10.2478/bile-2013-0013

Schlüsselwörter
combining models, Discrete Discriminant Analysis, variable selection

This content is open access.

Selection of variables in Discrete Discriminant Analysis

Online veröffentlicht: 05. Juni 2013

Seitenbereich: 1 - 14

DOI: https://doi.org/10.2478/bile-2013-0013

Schlüsselwörtercombining models, Discrete Discriminant Analysis, variable selection

This content is open access.

Schlüsselwörter
combining models, Discrete Discriminant Analysis, variable selection