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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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