<p><em>AStA Advances in Statistical Analysis</em></p>

<article class="align-center media media--type-link media--view-mode-default"><div class="field field--name-field-media-entity-link field--type-link field--label-hidden field__item"><a href="https://link.springer.com/article/10.1007/s10182-021-00391-y" target="_blank">105(2), pp. 247–271</a></div>
      
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A variable Selection Procedure for Depth Measures. AStA Advances in Statistical Analysis

A variable Selection Procedure for Depth Measures. AStA Advances in Statistical Analysis - https://link.springer.com/article/10.1007/s10182-021-00391-y

Abstract<br />
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.

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A variable Selection Procedure for Depth Measures. AStA Advances in Statistical Analysis

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