| bibtype |
J -
Journal Article
|
| ARLID |
0493763 |
| utime |
20240903121019.5 |
| mtime |
20180926235959.9 |
| SCOPUS |
85090693948 |
| WOS |
000557809200002 |
| title
(primary) (eng) |
Parametric Elliptical Regression Quantiles |
| specification |
| page_count |
27 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0362539 |
| ISSN |
1645-6726 |
| title
|
Revstat Statistical Journal |
| volume_id |
18 |
| volume |
3 (2020) |
| page_num |
257-280 |
| publisher |
|
|
| keyword |
multiple-output regression |
| keyword |
quantile regression |
| keyword |
nonlinear regression |
| keyword |
elliptical quantile |
| author
(primary) |
| ARLID |
cav_un_auth*0213091 |
| name1 |
Hlubinka |
| name2 |
D. |
| 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 |
GA17-07384S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0345381 |
|
| project |
| project_id |
GA14-07234S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0307008 |
|
| abstract
(eng) |
The article extends linear and nonlinear quantile regression to the case of vector responses by generalizing multivariate elliptical quantiles to a regression context. In particular, it introduces parametric elliptical quantile regression in a general nonlinear multivariate heteroscedastic framework and discusses, investigates, and illustrates the new method in some detail, including basic properties, various parametrizations, possible heteroscedastic patterns, related computational issues, model validation, and a real biometric data example. The method seems suitable for multi-response regression models with symmetric errors, especially if the dimension of responses is less than ten and if the right parametrization of the model follows from the context. |
| result_subspec |
WOS |
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10101 |
| reportyear |
2021 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0289349 |
| cooperation |
| ARLID |
cav_un_auth*0296001 |
| name |
Univerzita Karlova v Praze, Matematicko-fyzikální fakulta |
| institution |
MFF UK |
| country |
CZ |
|
| confidential |
S |
| mrcbC86 |
3+4 Article Statistics Probability |
| mrcbC91 |
A |
| mrcbT16-e |
STATISTICSPROBABILITY |
| mrcbT16-i |
0.00071 |
| mrcbT16-j |
0.403 |
| mrcbT16-s |
0.538 |
| mrcbT16-B |
16.716 |
| mrcbT16-D |
Q4 |
| mrcbT16-E |
Q3 |
| arlyear |
2020 |
| mrcbU14 |
85090693948 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000557809200002 WOS |
| mrcbU63 |
cav_un_epca*0362539 Revstat Statistical Journal 1645-6726 2183-0371 Roč. 18 č. 3 2020 257 280 INE |
|