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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/22592


    Title: Functional Clustering and Identifying Substructures of Longitudinal Data
    Authors: 李百靈;Li, Pai-ling
    Contributors: 淡江大學統計學系
    Date: 2007-06-27
    Issue Date: 2009-11-30 14:12:30 (UTC+8)
    Publisher: 中央研究院統計所; 主計處; 中國統計學社; 泛華統計學會(ICSA)
    Abstract: This study considers two clustering criteria to achieve different goals of grouping similar curves. These criteria are based on the minimal L2 distance and the maximal functional correlation defined in this study, respectively. Each cluster centers on a subspace spanned by the cluster mean and covariance eigenfunctions of the underlying random functions. Clusters can thus be identified by the subspace projection of curves.
    Relation: The 2007 Taipei International Statistical Symposium and ICSA International Conference
    Appears in Collections:[統計學系暨研究所] 會議論文

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