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Multivariate ranks based on randomized lift-interdirections

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00564920" target="_blank" >RIV/67985556:_____/22:00564920 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216208:11320/22:10451089

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S0167947322000603?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0167947322000603?via%3Dihub</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.csda.2022.107480" target="_blank" >10.1016/j.csda.2022.107480</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Multivariate ranks based on randomized lift-interdirections

  • Original language description

    Every multivariate sign and rank test needs a workable concept of ranks for multivariate data. Unfortunately, multidimensional spaces lack natural ordering and, consequently, there are no universally accepted ways how to rank vector observations. Existing proposals usable beyond small dimensions are very few in number, and each of them has its own advantages and drawbacks. Therefore, new multivariate ranks based on randomized lift-interdirections are presented, discussed and investigated. These naturally robust and invariant hyperplane-based ranks can be computed quickly and easily even in relatively high-dimensional spaces, and they can be used for nonparametric statistical inference in some existing optimal statistical procedures without altering their asymptotic behavior under null hypotheses or changing their performance under local alternatives. This is not only proved theoretically in case of the canonical sign and rank one-sample test for elliptically distributed observations, but also illustrated empirically in a small simulation study.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10103 - Statistics and probability

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Computational Statistics and Data Analysis

  • ISSN

    0167-9473

  • e-ISSN

    1872-7352

  • Volume of the periodical

    172

  • Issue of the periodical within the volume

    1

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    12

  • Pages from-to

    107480

  • UT code for WoS article

    000796740200004

  • EID of the result in the Scopus database

    2-s2.0-85127334374