期刊論文
| 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 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/20735 ) |