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Ieva, Francesca; Paganoni, Anna M.

Depth measures for multivariate functional data. (English) Zbl 1347.62093

Commun. Stat., Theory Methods 42, No. 7, 1265-1276 (2013).
Summary: In this article, we address the problem of mining and analyzing multivariate functional data. That is, data where each observation is a set of possibly correlated functions. Complex data of this kind is more and more common in many research fields, particularly in the biomedical context. In this work, we propose and apply a new concept of depth measure for multivariate functional data. With this new depth measure it is possible to generalize robust statistics, such as the median, to the multivariate functional framework, which in turn allows the application of outlier detection, boxplots construction, and nonparametric tests also in this more general framework. We present an application to Electrocardiographic (ECG) signals.

Cited in 13 Documents

MSC:

62H15 Hypothesis testing in multivariate analysis
62P10 Applications of statistics to biology and medical sciences; meta analysis
92C55 Biomedical imaging and signal processing

Keywords:

depth measures; ECG signals; multivariate functional data; rank tests
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\textit{F. Ieva} and \textit{A. M. Paganoni}, Commun. Stat., Theory Methods 42, No. 7, 1265--1276 (2013; Zbl 1347.62093)
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