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    Por favor, use este identificador para citar o enlazar este ítem:http://uvadoc.uva.es/handle/10324/32023

    Título
    Finding the number of normal groups in model-based clustering via constrained likelihoods
    Autor
    Cerioli, Andrea
    García Escudero, Luis ÁngelAutoridad UVA Orcid
    Mayo Iscar, AgustínAutoridad UVA Orcid
    Riani, Marco
    Año del Documento
    2018
    Editorial
    Taylor & Francis
    Descripción
    Producción Científica
    Documento Fuente
    Journal of Computational and Graphical Statistics, 2016, vol. 27, p. 404-416
    Resumen
    Deciding the number of clusters k is one of the most difficult problems in clus- ter analysis. For this purpose, complexity-penalized likelihood approaches have been introduced in model-based clustering, such as the well known BIC and ICL crite- ria. However, the classi cation/mixture likelihoods considered in these approaches are unbounded without any constraint on the cluster scatter matrices. Constraints also prevent traditional EM and CEM algorithms from being trapped in (spurious) local maxima. Controlling the maximal ratio between the eigenvalues of the scatter matrices to be smaller than a xed constant c 1 is a sensible idea for setting such constraints. A new penalized likelihood criterion which takes into account the higher model complexity that a higher value of c entails, is proposed. Based on this criterion, a novel and fully automated procedure, leading to a small ranked list of optimal (k; c) couples is provided. A new plot called \car-bike" which provides a concise summary of the solutions is introduced. The performance of the procedure is assessed both in empirical examples and through a simulation study as a function of cluster overlap. Supplemental materials for the article are available online.
    Palabras Clave
    BIC
    CEM algorithm
    Clustering
    EM algorithm
    ICL
    Mixtures
    ISSN
    1061-8600
    Revisión por pares
    SI
    DOI
    10.1080/10618600.2017.1390469
    Patrocinador
    Spanish Ministerio de Economía y Competitividad, grant MTM2017-86061-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León and FEDER, grant VA005P17 and VA002G18.
    Version del Editor
    https://www.tandfonline.com/doi/full/10.1080/10618600.2017.1390469
    Propietario de los Derechos
    © 2018 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
    Idioma
    eng
    URI
    http://uvadoc.uva.es/handle/10324/32023
    Tipo de versión
    info:eu-repo/semantics/acceptedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP24 - Artículos de revista [78]
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