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

Author

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  • Tarabelloni Nicholas
  • Biasi Rachele
  • Maria Paganoni Anna

    (Department of Mathematics, Politecnico di Milano, Milano, Italy)

  • Ieva Francesca

    (Department of Mathematics, Università degli Studi di Milano, Milano, Italy)

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.

Suggested Citation

  • Tarabelloni Nicholas & Biasi Rachele & Maria Paganoni Anna & Ieva Francesca, 2015. "Use of Depth Measure for Multivariate Functional Data in Disease Prediction: An Application to Electrocardiograph Signals," The International Journal of Biostatistics, De Gruyter, vol. 11(2), pages 189-201, November.
    • Handle: RePEc:bpj:ijbist:v:11:y:2015:i:2:p:189-201:n:3
    DOI: 10.1515/ijb-2014-0041
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    References listed on IDEAS

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    1. López-Pintado, Sara & Romo, Juan, 2009. "On the Concept of Depth for Functional Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 718-734.
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