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. A simulation studies is carried out to validate the robustness of the proposed methods. 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.

Use of depth measure for multivariate functional data in disease prediction: an application to electrocardiograph signals / N. Tarabelloni, F. Ieva, R. Biasi, A. Paganoni. - In: THE INTERNATIONAL JOURNAL OF BIOSTATISTICS. - ISSN 1557-4679. - 11:2(2015 Nov), pp. 189-201. [10.1515/ijb-2014-0041]

Use of depth measure for multivariate functional data in disease prediction: an application to electrocardiograph signals

F. Ieva
Secondo
;
2015

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. A simulation studies is carried out to validate the robustness of the proposed methods. 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.
Depth measures; multivariate functional data; covariance operators; ECG signals, generalized linear models
Settore SECS-S/01 - Statistica
Settore MAT/06 - Probabilita' e Statistica Matematica
Settore MED/01 - Statistica Medica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/286503
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