Partial reconstruction of measures from halfspace depth
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10452439" target="_blank" >RIV/00216208:11320/21:10452439 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-30164-3_8" target="_blank" >https://doi.org/10.1007/978-3-031-30164-3_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-30164-3_8" target="_blank" >10.1007/978-3-031-30164-3_8</a>
Alternative languages
Result language
angličtina
Original language name
Partial reconstruction of measures from halfspace depth
Original language description
The halfspace depth of a d-dimensional point x with respect to a finite (or probability) Borel measure μ in Rd is defined as the infimum of the μ-masses of all closed halfspaces containing x. A natural question is whether the halfspace depth, as a function of xELEMENT OFRd, determines the measure μ completely. In general, it turns out that this is not the case, and it is possible for two different measures to have the same halfspace depth function everywhere in Rd. In this paper we show that despite this negative result, one can still obtain a substantial amount of information on the support and the location of the mass of μ from its halfspace depth. We illustrate our partial reconstruction procedure in an example of a non-trivial bivariate probability distribution whose atomic part is determined successfully from its halfspace depth.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/GX19-28231X" target="_blank" >GX19-28231X: DyMoDiF - Dynamic Models for the Digital Finance</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Statistical Models and Methods for Data Science
ISBN
978-3-031-30163-6
ISSN
1431-8814
e-ISSN
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Number of pages
13
Pages from-to
93-105
Publisher name
Springer
Place of publication
Cham
Event location
Firenze
Event date
Sep 9, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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