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Por favor, use este identificador para citar o enlazar este ítem: http://uvadoc.uva.es/handle/10324/21814
Título: Robustness and Outliers
Autor: García Escudero, Luis Ángel
Gordaliza, Alfonso
Matrán Bea, Carlos
Mayo Iscar, Agustín
Hennig, Ch.
Año del Documento: 2015
Editorial: Chapman and Hall/CRC
Descripción: Producción Científica
Documento Fuente: Handbook of Cluster Analysis. Eds.: Christian Hennig, Marina Meila, Fionn Murtagh, Roberto Rocci. Chapman and Hall/CRC, 2015. p. 653-678 (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)
Resumen: Unexpected deviations from assumed models as well as the presence of certain amounts of outlying data are common in most practical statistical applications. This fact could lead to undesirable solutions when applying non-robust statistical techniques. This is often the case in cluster analysis, too. The search for homogeneous groups with large heterogeneity between them can be spoiled due to the lack of robustness of standard clustering methods. For instance, the presence of (even few) outlying observations may result in heterogeneous clusters artificially joined together or in the detection of spurious clusters merely made up of outlying observations. In this chapter we will analyze the effects of different kinds of outlying data in cluster analysis and explore several alternative methodologies designed to avoid or minimize their undesirable effects.
Materias (normalizadas): statistical applications
ISBN: 9781466551886
Patrocinador: Ministerio de Economía, Industria y Competitividad (MTM2014-56235-C2-1-P)
Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA212U13)
Version del Editor: https://www.crcpress.com/
Idioma: eng
URI: http://uvadoc.uva.es/handle/10324/21814
Derechos: info:eu-repo/semantics/openAccess
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