Multivariate ranks based on randomized lift-interdirections

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11320/22:10451089

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Multivariate ranks based on randomized lift-interdirections

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multivariate ranks based on randomized lift-interdirections

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Computational Statistics and Data Analysis

ISSN

0167-9473

e-ISSN

1872-7352

Svazek periodika

172

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

107480

Kód UT WoS článku

000796740200004

EID výsledku v databázi Scopus

2-s2.0-85127334374