| bibtype |
J -
Journal Article
|
| ARLID |
0546694 |
| utime |
20230418204308.9 |
| mtime |
20211015235959.9 |
| SCOPUS |
85116552764 |
| WOS |
000705729800001 |
| DOI |
10.1007/s11634-021-00471-6 |
| title
(primary) (eng) |
The minimum weighted covariance determinant estimator for high-dimensional data |
| specification |
| page_count |
23 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0361698 |
| ISSN |
1862-5347 |
| title
|
Advances in Data Analysis and Classification |
| volume_id |
16 |
| volume |
4 (2022) |
| page_num |
977-999 |
| publisher |
|
|
| keyword |
High-dimensional data |
| keyword |
Regularization |
| keyword |
Robust estimation |
| keyword |
Implicit weighting |
| keyword |
Scatter matrix |
| author
(primary) |
| ARLID |
cav_un_auth*0345793 |
| name1 |
Kalina |
| name2 |
Jan |
| institution |
UTIA-B |
| full_dept (cz) |
Stochastická informatika |
| full_dept (eng) |
Department of Stochastic Informatics |
| department (cz) |
SI |
| department (eng) |
SI |
| full_dept |
Department of Stochastic Informatics |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0387590 |
| name1 |
Tichavský |
| name2 |
J. |
| country |
CZ |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA21-05325S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0409039 |
|
| project |
| project_id |
GA19-05704S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0375756 |
|
| abstract
(eng) |
In a variety of diverse applications, it is very desirable to perform a robust analysis of high-dimensional measurements without being harmed by the presence of a possibly larger percentage of outlying measurements. The minimum weighted covariance determinant (MWCD) estimator, based on implicit weights assigned to individual observations, represents a promising and flexible extension of the popular minimum covariance determinant (MCD) estimator of the expectation and scatter matrix of mlutivariate data. In this work, a regularized version of the MWCD denoted as the minimum regularized weighted covariance determinant (MRWCD) estimator is proposed. At the same time, it is accompanied by an outlier detection procedure. The novel MRWCD estimator is able to outperform other available robust estimators in several simulation scenarios, especially in estimating the scatter matrix of contaminated high-dimensional data. |
| result_subspec |
WOS |
| RIV |
BA |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10101 |
| reportyear |
2023 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0323102 |
| confidential |
S |
| mrcbC91 |
C |
| mrcbT16-e |
STATISTICSPROBABILITY |
| mrcbT16-j |
0.943 |
| mrcbT16-s |
0.853 |
| mrcbT16-D |
Q2 |
| mrcbT16-E |
Q2 |
| arlyear |
2022 |
| mrcbU14 |
85116552764 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000705729800001 WOS |
| mrcbU63 |
cav_un_epca*0361698 Advances in Data Analysis and Classification 1862-5347 1862-5355 Roč. 16 č. 4 2022 977 999 Springer |
|