Density kernel depth for outlier detection in functional data

Nicolás Hernández; Alberto Mu?oz; Gabriel Martos

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Density kernel depth for outlier detection in functional data

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Abstract In this paper, we propose a novel approach to address the problem of functional outlier detection. Our method leverages a low-dimensional and stable representation of functions using Reproducing Kernel Hilbert Spaces (RKHS). We define a depth measure based on density kernels that satisfy desirable properties. We also address the challenges associated with estimating the density kernel depth. Throughout a Monte Carlo simulation we assess the performance of our functional depth measure in the outlier detection task under different scenarios. To illustrate the effectiveness of our method, we showcase the proposed method in action studying outliers in mortality rate curves.

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《International Journal of Data Science and Analytics》 |2023年第4期|481-488|共8页
作者
Nicolás Hernández; Alberto Mu?oz; Gabriel Martos;
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作者单位

Universidad Carlos III de Madrid;

Universidad Torcuato Di Tella;

University College London;

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原文格式 PDF
正文语种英语
中图分类计算技术、计算机技术;
关键词
Functional data; Depth measures; Outlier detection; Mortality curves;

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Density kernel depth for outlier detection in functional data

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