| bibtype |
J -
Journal Article
|
| ARLID |
0564920 |
| utime |
20230321161952.1 |
| mtime |
20221202235959.9 |
| SCOPUS |
85127334374 |
| WOS |
000796740200004 |
| DOI |
10.1016/j.csda.2022.107480 |
| title
(primary) (eng) |
Multivariate ranks based on randomized lift-interdirections |
| specification |
| page_count |
12 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0256439 |
| ISSN |
0167-9473 |
| title
|
Computational Statistics and Data Analysis |
| volume_id |
172 |
| publisher |
|
|
| keyword |
multivariate rank |
| keyword |
lift-interdirection |
| keyword |
interdirection |
| keyword |
rank test |
| keyword |
one-sample test |
| keyword |
robustness |
| author
(primary) |
| ARLID |
cav_un_auth*0385823 |
| name1 |
Hudecová |
| name2 |
Š. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0266474 |
| name1 |
Šiman |
| name2 |
Miroslav |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| full_dept |
Department of Stochastic Informatics |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA21-05325S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0409039 |
|
| project |
| project_id |
GF22-01639K |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0440862 |
|
| abstract
(eng) |
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. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2023 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0336508 |
| confidential |
S |
| article_num |
107480 |
| mrcbC86 |
n.a. Article Computer Science Interdisciplinary Applications|Statistics Probability |
| mrcbC91 |
C |
| mrcbT16-e |
COMPUTERSCIENCEINTERDISCIPLINARYAPPLICATIONS|STATISTICSPROBABILITY |
| mrcbT16-j |
0.947 |
| mrcbT16-s |
0.877 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q2 |
| arlyear |
2022 |
| mrcbU14 |
85127334374 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000796740200004 WOS |
| mrcbU63 |
cav_un_epca*0256439 Computational Statistics and Data Analysis 0167-9473 1872-7352 Roč. 172 č. 1 2022 Elsevier |
|