Use of Depth Measure for Multivariate Functional Data in Disease Prediction: An Application to Electrocardiograph Signals

Tarabelloni Nicholas, Ieva Francesca (), Biasi Rachele and Maria Paganoni Anna
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Tarabelloni Nicholas: Department of Mathematics, Politecnico di Milano, Milano, Italy
Ieva Francesca: Department of Mathematics, Università degli Studi di Milano, Milano, Italy
Biasi Rachele: Department of Mathematics, Politecnico di Milano, Milano, Italy
Maria Paganoni Anna: Department of Mathematics, Politecnico di Milano, Milano, Italy

The International Journal of Biostatistics, 2015, vol. 11, issue 2, 189-201

Abstract: In this paper we develop statistical methods to compare two independent samples of multivariate functional data that differ in terms of covariance operators. In particular we generalize the concept of depth measure to this kind of data, exploiting the role of the covariance operators in weighting the components that define the depth. Two simulation studies are carried out to validate the robustness of the proposed methods and to test their effectiveness in some settings of interest. We present an application to Electrocardiographic (ECG) signals aimed at comparing physiological subjects and patients affected by Left Bundle Branch Block. The proposed depth measures computed on data are then used to perform a nonparametric comparison test among these two populations. They are also introduced into a generalized regression model aimed at classifying the ECG signals.

Keywords: depth measures; multivariate functional data; covariance operators; ECG signals; generalized linear models (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/ijb-2014-0041

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