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