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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/50439


    Title: Correlation-Based Functional Clustering via Subspace Projection
    Authors: Chiou, Jeng-min;Li, Pai-ling
    Contributors: 淡江大學統計學系
    Keywords: Functional correlation;Functional data;Functional principal component analysis;Projection;Random scale effects;Shape similarity
    Date: 2008-12
    Issue Date: 2010-08-09 17:26:37 (UTC+8)
    Publisher: Alexandria: American Statistical Association
    Abstract: A correlation-based functional clustering method is proposed for grouping curves with similar shapes. A correlation between two random functions defined through the functional inner product is used as a similarity measure. Curves with similar shapes are embedded in the cluster subspace spanned by a mean shape function and eigenfunctions of the covariance kernel. The cluster membership prediction for each curve attempts to maximize the functional correlation between the observed and predicted curves via shape standardization and subspace projection among all possible clusters. The proposed method accounts for shape differentials through the functional multiplicative random-effects shape function model for each cluster, which regards random scales and intercept shifts as a nuisance. A consistent estimate is proposed for the random scale effect, whose sample variance estimate is also consistent. The derived identifiability conditions for the clustering procedure unravel the predictability of cluster memberships. Simulation studies and a real data example illustrate the proposed method.
    Relation: Journal of the American Statistical Association 103(484), pp.1684-1692
    DOI: 10.1198/016214508000000814
    Appears in Collections:[Graduate Institute & Department of Statistics] Journal Article

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