We herein introduce variable selection procedures based on depth similarity, aimed at identifying a small subset of variables that can better explain the depth assigned to each point in space. Our study is not intended to deal with the case of high-dimensional data. Identifying noisy and dependent variables helps us understand the underlying distribution of a given dataset. The asymptotic behaviour of the proposed methods and numerical aspects concerning the computational burden are studied. Furthermore, simulations and a real data example are analysed.

中文翻译：

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深度测量的变量选择程序

我们在此介绍基于深度相似性的变量选择过程，旨在识别变量的一小部分子集，这些子集可以更好地解释分配给空间中每个点的深度。我们的研究并非旨在处理高维数据的情况。识别嘈杂变量和因变量有助于我们了解给定数据集的基本分布。研究了所提出方法的渐近行为以及与计算负担有关的数值方面。此外，还分析了仿真和实际数据示例。