A variable selection procedure for depth measures

Agustín Alvarez () and Marcela Svarc
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Agustín Alvarez: Universidad Nacional de General Sarmiento
Marcela Svarc: Universidad de San Andrés and CONICET

AStA Advances in Statistical Analysis, 2021, vol. 105, issue 2, No 3, 247-271

Abstract: Abstract 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.

Keywords: Feature extraction; Multivariate data analysis; Data depth; Dimension reduction; 62H99 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10182-021-00391-y

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