Functional clustering and identifying substructures of longitudinal data
學年 96
學期 1
出版（發表）日期 2007-09-01
作品名稱 Functional clustering and identifying substructures of longitudinal data
作品名稱（其他語言）
著者 Chiou, Jeng-Min; Li, Pai-ling
單位 淡江大學統計學系
出版者 Chichester: Wiley-Blackwell Publishing Ltd.
著錄名稱、卷期、頁數 Journal of the Royal Statistical Society B 69(4), pp.679-699
摘要 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.
關鍵字 Classification; Clustering; Functional data; Functional principal component analysis; Modes of variation; Stochastic processes
語言 en
ISSN 1369-7412 1467-9868
期刊性質 國外
收錄於 SCI SSCI
產學合作
通訊作者 Chiou, Jeng-Min
審稿制度 是
國別 GBR
公開徵稿
出版型式 電子版 紙本
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