Archetypal analysis for ordinal data
Impacto
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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INVESTIGACIONMetadatos
Título
Archetypal analysis for ordinal dataFecha de publicación
2021-08-03Editor
Elsevier Inc.ISSN
0020-0255Cita bibliográfica
Fernández, D., Epifanio, I., & McMillan, L. F. (2021). Archetypal analysis for ordinal data. Information Sciences, 579, 281-292.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Archetypoid analysis (ADA) is an exploratory approach that explains a set of continuous observations as mixtures of pure (extreme) patterns. Those patterns (archetypoids) are actual observations of the sample which ... [+]
Archetypoid analysis (ADA) is an exploratory approach that explains a set of continuous observations as mixtures of pure (extreme) patterns. Those patterns (archetypoids) are actual observations of the sample which makes the results of this technique easily interpretable, even for non-experts. Note that the observations are approximated as a convex combination of the archetypoids. Archetypoid analysis, in its current form, cannot be applied directly to ordinal data. We propose and describe a two-step method for applying ADA to ordinal responses based on the ordered stereotype model. One of the main advantages of this model is that it allows us to convert the ordinal data to numerical values, using a new data-driven spacing that better reflects the ordinal patterns of the data, and this numerical conversion then enables us to apply ADA straightforwardly. The results of the novel method are presented for two behavioural science applications. Finally, the proposed method is also compared with other unsupervised statistical learning methods. [-]
Publicado en
Information Sciences, Vol. 579 (November 2021)Entidad financiadora
Departament d’Economia i Coneixement (Generalitat de Catalunya) | Royal Society (New Zealand) | Ministerio de Ciencia e Innovación (España) | Universitat Jaume I
Código del proyecto o subvención
SGR 622 (GRBIO) | E2987-3648 | DPI2017-87333-R | UJI-B2020-22
Derechos de acceso
© 2021 The Author(s).
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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