Reconstruction of atomic measures from their halfspace depth

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10434778" target="_blank" >RIV/00216208:11320/21:10434778 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=WK3saMylph" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=WK3saMylph</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jmva.2021.104727" target="_blank" >10.1016/j.jmva.2021.104727</a>

Result language
angličtina
Original language name
Reconstruction of atomic measures from their halfspace depth
Original language description
The halfspace depth can be seen as a mapping that to a finite Borel measure mu on the Euclidean space R-d assigns its depth, being a function R-d -> [0, infinity): x -> D (x; mu). The depth of mu quantifies how much centrally positioned a point x is with respect to mu. This function is intended to serve as generalization of the quantile function to multivariate spaces. We consider the problem of finding the inverse mapping to the halfspace depth: knowing only the function x -> D (x; mu), our objective is to reconstruct the measure mu. We focus on mu atomic with finitely many atoms, and present a simple method for the reconstruction of the position and the weights of all atoms of mu, from its depth only. As a consequence, (i) we recover generalizations of several related results known from the literature, with substantially simplified proofs, and (ii) design a novel reconstruction procedure that is numerically more stable, and considerably faster than the known algorithms. Our analysis presents a comprehensive treatment of the halfspace depth of those measures whose depths attain finitely many different values. (C) 2021 Elsevier Inc. All rights reserved.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GJ19-16097Y" target="_blank" >GJ19-16097Y: Geometric aspects of mathematical statistics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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