Selection of variables in Discrete Discriminant Analysis

• Autorzy: Anabela Marques , Ana Sousa Ferreira , Margarida G.M.S. Cardoso
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

combining models; Discrete Discriminant Analysis; variable selection

• Wydawca: De Gruyter Open
• Czasopismo: Biometrical Letters
• Rocznik: 2013
• Tom: 50
• Numer: 1
• Strony: 1-14

Daty

wydano

2013-06-01

online

2013-06-05

Twórcy

autor

Anabela Marques

• Barreiro College of Technology, Setúbal Polytechnic, IPS, Portugal

autor

Ana Sousa Ferreira

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

autor

Margarida G.M.S. Cardoso

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

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Identyfikatory

DOI

10.2478/bile-2013-0013