出版日期：2007-09-01 00:00:00

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

通訊作者：Chiou, Jeng-Min

審稿制度：是

國別：GBR

出版型式：電子版 紙本