Discriminant Analysis of Interval Data: An Assessment of Parametric and Distance-Based Approaches

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Silva, APD (Autor) Outra; brito, p (Autor) FEP

Revista

Título: Journal of Classification

Vol. 32

Páginas: 516-541

ISSN: 0176-4268

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 21 Citações

Scopus - 23 Citações

Outras Informações

ID Authenticus: P-00G-XE3

DOI: 10.1007/s00357-015-9189-8

Abstract (EN): Building on probabilistic models for interval-valued variables, parametric classification rules, based on Normal or Skew-Normal distributions, are derived for interval data. The performance of such rules is then compared with distancebased methods previously investigated. The results show that Gaussian parametric approaches outperform Skew-Normal parametric and distance-based ones in most conditions analyzed. In particular, with heterocedastic data a quadratic Gaussian rule always performs best. Moreover, restricted cases of the variance-covariance matrix lead to parsimonious rules which for small training samples in heterocedastic problems can outperform unrestricted quadratic rules, even in some cases where the model assumed by these rules is not true. These restrictions take into account the particular nature of interval data, where observations are defined by both MidPoints and Ranges, which may or may not be correlated. Under homocedastic conditions linear Gaussian rules are often the best rules, but distance-based methods may perform better in very specific conditions.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 26