Common Multivariate Estimators of Location and Scatter Capture the Symmetry of the Underlying Distribution
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00504387" target="_blank" >RIV/67985807:_____/21:00504387 - isvavai.cz</a>
Alternative codes found
RIV/67985556:_____/21:00583622
Result on the web
<a href="http://dx.doi.org/10.1080/03610918.2019.1615624" target="_blank" >http://dx.doi.org/10.1080/03610918.2019.1615624</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1080/03610918.2019.1615624" target="_blank" >10.1080/03610918.2019.1615624</a>
Alternative languages
Result language
angličtina
Original language name
Common Multivariate Estimators of Location and Scatter Capture the Symmetry of the Underlying Distribution
Original language description
The article discusses how various multivariate location and scatter estimators capture the symmetry of the underlying distribution. Very general sufficient conditions are formulated, which ensure various symmetry properties of functionals corresponding to location or scatter. Examples of robust multivariate estimators, which fulfill these conditions, are discussed in detail. The obtained symmetry of the estimators is applicable to hypothesis tests of symmetry of the underlying distribution of the multivariate data. For this task, we propose to perform permutation tests exploiting the nonparametric combination methodology. The performance of the newly proposed tests is illustrated on simulated as well as real data. The tests are suitable for small sample sizes and represent the first available symmetry tests suitable also for non-elliptical distributions and for more than just two variables.
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
<a href="/en/project/GA17-07384S" target="_blank" >GA17-07384S: Nonparametric (statistical) methods in modern econometrics</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Communications in Statistics - Simulation and Computation
ISSN
0361-0918
e-ISSN
1532-4141
Volume of the periodical
50
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
2845-2857
UT code for WoS article
000469602900001
EID of the result in the Scopus database
2-s2.0-85066100659