Functional clustering and identifying substructures of longitudinal data
Jeng‐Min Chiou and
Pai‐Ling Li
Journal of the Royal Statistical Society Series B, 2007, vol. 69, issue 4, 679-699
Abstract:
Summary. 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.
Date: 2007
References: View complete reference list from CitEc
Citations: View citations in EconPapers (46)
Downloads: (external link)
https://doi.org/10.1111/j.1467-9868.2007.00605.x
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssb:v:69:y:2007:i:4:p:679-699
Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9868
Access Statistics for this article
Journal of the Royal Statistical Society Series B is currently edited by P. Fryzlewicz and I. Van Keilegom
More articles in Journal of the Royal Statistical Society Series B from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().