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Selection of variables in Discrete Discriminant Analysis

100%
Marques A., Sousa Ferreira A., Cardoso M. G. M. S.
Biometrical Letters
|
2013
|
tom 50
|
nr 1
1-14
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.
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Comparison of Multivariate Analysis Methodologies in a Palliative Care Setting

88%
Pinto Doria I., Sousa Ferreira A., Dias O., Bacelar-Nicolau H., Le Calvé G.
Biometrical Letters
|
2012
|
tom 49
|
nr 1
67-79
EN
This study is focused on measuring the quality and the satisfaction with the palliative care provided to oncology patients in domicile. The SERVQUAL methodology adapted for the Portuguese context was used to evaluate the quality of palliative care and patient satisfaction. The Portuguese SERVQUAL questionnaire is composed of five perception scales and two questionnaires, one about the patient and another about the caregiver. The data analysis presented is the analysis of the answers to the five perception scales, composed of partial ordered variables, evaluating different aspects of quality and satisfaction.The data was analysed comparing metric and symbolic approaches, using Principal Component Analysis Methods and Agglomerative Hierarchical Cluster Analysis Models. The results suggest that a symbolic approach provides a more comprehensive analysis for this kind of data.
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