Depth-based classification for functional data
Depth-based classification for functional data
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DOI：
10.1090/dimacs/072/08
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发表时间：
2005-10
期刊：
影响因子：
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通讯作者：
S. López-Pintado;J. Romo
中科院分区：
文献类型：
--
作者：
S. López-Pintado;J. Romo
当数据是曲线时，分类是一项重要的任务。最近，统计深度的概念已经扩展到处理功能观测。本文提出了基于深度概念的鲁棒曲线分类方法。这些技术被应用到一个真实的数据示例中。对污染模型的大量仿真研究表明，这些基于深度的分类方法具有良好的鲁棒性。
Classification is an important task when data are curves. Recently, the notion of statistical depth has been extended to deal with functional observations. In this paper, we propose robust procedures based on the concept of depth to classify curves. These techniques are applied to a real data example. An extensive simulation study with contaminated models illustrates the good robustness properties of these depth-based classification methods.