| bibtype |
J -
Journal Article
|
| ARLID |
0474696 |
| utime |
20240103214056.9 |
| mtime |
20170522235959.9 |
| SCOPUS |
85009231891 |
| WOS |
000406683400006 |
| DOI |
10.1007/s00180-016-0708-9 |
| title
(primary) (eng) |
On weighted and locally polynomial directional quantile regression |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0252572 |
| ISSN |
0943-4062 |
| title
|
Computational Statistics |
| volume_id |
32 |
| volume |
3 (2017) |
| page_num |
929-946 |
| publisher |
|
|
| keyword |
Quantile regression |
| keyword |
Nonparametric regression |
| keyword |
Nonparametric regression |
| author
(primary) |
| ARLID |
cav_un_auth*0101069 |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| name1 |
Boček |
| name2 |
Pavel |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0266474 |
| full_dept (cz) |
Stochastická informatika |
| full_dept |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department |
SI |
| full_dept |
Department of Stochastic Informatics |
| name1 |
Šiman |
| name2 |
Miroslav |
| institution |
UTIA-B |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0307008 |
| project_id |
GA14-07234S |
| agency |
GA ČR |
|
| abstract
(eng) |
The article deals with certain quantile regression methods for vector responses. In particular, it describes weighted and locally polynomial extensions to the projectional quantile regression, discusses their properties, addresses their computational side, compares their outcome with recent analogous generalizations of the competing multiple-output directional quantile regression, demonstrates a link between the two competing methodologies, complements the results already available in the literature, illustrates the concepts with a few simulated and insightful examples illustrating some of their features, and shows their application to a real financial data set, namely to Forex 1M exchange rates. The real-data example strongly indicates that the presented methods might have a huge impact on the analysis of multivariate time series consisting of two to four dimensional observations. |
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2018 |
| num_of_auth |
2 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0271770 |
| confidential |
S |
| mrcbC86 |
2 Article Statistics Probability |
| mrcbC86 |
2 Article Statistics Probability |
| mrcbC86 |
2 Article Statistics Probability |
| mrcbT16-e |
STATISTICSPROBABILITY |
| mrcbT16-j |
0.611 |
| mrcbT16-s |
0.803 |
| mrcbT16-B |
43.288 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q3 |
| arlyear |
2017 |
| mrcbU14 |
85009231891 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000406683400006 WOS |
| mrcbU63 |
cav_un_epca*0252572 Computational Statistics 0943-4062 1613-9658 Roč. 32 č. 3 2017 929 946 Springer |
|