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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/20735


    Title: Functional clustering and identifying substructures of longitudinal data
    Authors: Chiou, Jeng-Min;Li, Pai-ling
    Contributors: 淡江大學統計學系
    Keywords: Classification;Clustering;Functional data;Functional principal component analysis;Modes of variation;Stochastic processes
    Date: 2007-09-01
    Issue Date: 2009-11-30 12:58:10 (UTC+8)
    Publisher: Chichester: Wiley-Blackwell Publishing Ltd.
    Abstract: A functional clustering (FC) method, k-centres FC, for longitudinal data is proposed. The k-centres FC approach accounts for both the means and the modes of variation differentials between clusters by predicting cluster membership with a reclassification step. The cluster membership predictions are based on a non-parametric random-effect model of the truncated Karhunen–Loève expansion, coupled with a non-parametric iterative mean and covariance updating scheme. We show that, under the identifiability conditions derived, the k-centres FC method proposed can greatly improve cluster quality as compared with conventional clustering algorithms. Moreover, by exploring the mean and covariance functions of each cluster, thek-centres FC method provides an additional insight into cluster structures which facilitates functional cluster analysis. Practical performance of the k-centres FC method is demonstrated through simulation studies and data applications including growth curve and gene expression profile data.
    Relation: Journal of the Royal Statistical Society B 69(4), pp.679-699
    DOI: 10.1111/j.1467-9868.2007.00605.x
    Appears in Collections:[統計學系暨研究所] 期刊論文

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