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    题名: Correlation-Based Functional Clustering via Subspace Projection
    作者: Chiou, Jeng-min;Li, Pai-ling
    贡献者: 淡江大學統計學系
    关键词: Functional correlation;Functional data;Functional principal component analysis;Projection;Random scale effects;Shape similarity
    日期: 2008-12
    上传时间: 2010-08-09 17:26:37 (UTC+8)
    出版者: Alexandria: American Statistical Association
    摘要: 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.
    關聯: Journal of the American Statistical Association 103(484), pp.1684-1692
    DOI: 10.1198/016214508000000814
    显示于类别:[統計學系暨研究所] 期刊論文


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