Model-based clustering for multivariate functional data

被引：179
|
作者：
Jacques, Julien [1 ]
Preda, Cristian
机构：
[1] Univ Lille 1, Lab Paul Painleve, UMR CNRS 8524, Lille, France
来源：
COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2014年 / 71卷
关键词：
Multivariate functional data; Density approximation; Model-based clustering; Multivariate functional principal component analysis; EM-algorithm;
D O I：
10.1016/j.csda.2012.12.004
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The first model-based clustering algorithm for multivariate functional data is proposed. After introducing multivariate functional principal components analysis (MFPCA), a parametric mixture model, based on the assumption of normality of the principal component scores, is defined and estimated by an EM-like algorithm. The main advantage of the proposed model is its ability to take into account the dependence among curves. Results on simulated and real datasets show the efficiency of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：92 / 106
页数：15
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