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

IF 1 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Biostatistics Pub Date : 2015-11-01 DOI:10.1515/ijb-2014-0041

Nicholas Tarabelloni, Francesca Ieva, Rachele Biasi, Anna Maria Paganoni

引用次数: 8

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.

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多变量功能数据深度测量在疾病预测中的应用:在心电图信号中的应用。

在本文中，我们开发了统计方法来比较在协方差算子方面不同的多元函数数据的两个独立样本。特别地，我们将深度度量的概念推广到这类数据，利用协方差算子在定义深度的分量加权中的作用。进行了两个仿真研究，以验证所提出的方法的鲁棒性，并在一些感兴趣的设置中测试其有效性。我们提出了一种应用于心电图(ECG)信号的方法，旨在比较生理受试者和受左束支传导阻滞影响的患者。根据数据计算得到的建议深度度量，然后用于在这两个种群之间进行非参数比较检验。并将它们引入到广义回归模型中，用于心电信号的分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

2.10

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.