| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0583575 |
| utime |
20240402215301.7 |
| mtime |
20240305235959.9 |
| title
(primary) (eng) |
Some Robust Approaches to Reducing the Complexity of Economic Data |
| specification |
| page_count |
10 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0581698 |
| ISBN |
978-80-87990-31-5 |
| title
|
The 17th International Days of Statistics and Economics Conference Proceedings |
| page_num |
246-255 |
| publisher |
| place |
Praha |
| name |
Melandrium |
| year |
2023 |
|
| editor |
|
| editor |
|
|
| keyword |
dimensionality reduction |
| keyword |
Big Data |
| keyword |
variable selection |
| keyword |
robustness |
| keyword |
sparsity |
| 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. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA21-05325S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0409039 |
|
| abstract
(eng) |
The recent advent of complex (and potentially big) data in economics requires modern and effective tools for their analysis including tools for reducing the dimensionality (complexity) of the given data. This paper starts with recalling the importance of Big Data in economics and with characterizing the main categories of dimension reduction techniques. While there have already been numerous techniques for dimensionality reduction available, this work is interested in methods that are robust to the presence of outlying measurements (outliers) in the economic data. Particularly, methods based on implicit weighting assigned to individual observations are developed in this paper. As the main contribution, this paper proposes three novel robust methods of dimension reduction. One method is a dimension reduction within a robust regularized linear regression, namely a sparse version of the least weighted squares estimator. The other two methods are robust versions of feature extraction methods popular in econometrics: robust principal component analysis and robust factor analysis. |
| action |
| ARLID |
cav_un_auth*0462041 |
| name |
International Days of Statistics and Economics /17./ |
| dates |
20230907 |
| mrcbC20-s |
20230909 |
| place |
Praha |
| country |
CZ |
|
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2024 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0351582 |
| confidential |
S |
| arlyear |
2023 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0581698 The 17th International Days of Statistics and Economics Conference Proceedings Melandrium 2023 Praha 246 255 978-80-87990-31-5 |
| mrcbU67 |
Löster T. 340 |
| mrcbU67 |
Pavelka T. 340 |
|