| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0642816 |
| utime |
20260123171641.2 |
| mtime |
20251209235959.9 |
| SCOPUS |
105023988800 |
| DOI |
10.1109/IJCNN64981.2025.11227341 |
| title
(primary) (eng) |
Local Sensitivity Analysis of Highly Robust Regression Estimators |
| specification |
| page_count |
8 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0642775 |
| ISBN |
979-8-3315-1042-8 |
| title
|
IJCNN 2025: International Joint Conference on Neural Networks Conference Proceedings |
| publisher |
| place |
Piscataway |
| name |
IEEE |
| year |
2025 |
|
|
| keyword |
outliers |
| keyword |
robustness |
| keyword |
linear regression |
| keyword |
implicit weighting |
| keyword |
local sensitivity |
| keyword |
subsample sensitivity |
| 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 |
| abstract
(eng) |
Robust regression methods are essential for estimating parameters in linear regression models, especially when data is contaminated by outliers or small fluctuations, common in real-world applications. This paper investigates the local sensitivity of robust regression estimators, focusing on how small modifications in the data affect their predictions. Local sensitivity analysis (LSA) offers valuable insights into the robustness of regression models, a crucial property in decision-making fields like economics, finance, and engineering, where data integrity is often compromised. We examine robust estimators based on implicit weights, including the least trimmed squares (LTS) and least weighted squares (LWS) estimators, along with their regularized versions. A novel adaptive version of the regularized LWS estimator is proposed, incorporating data-driven weights. Experiments on publicly available datasets show that while the LTS and LTS-lasso estimators exhibit high local sensitivity, LWS and LWS-lasso estimators—especially with adaptive weights—demonstrate superior robustness and predictive performance. These findings highlight the importance of considering local sensitivity in robust regression models, particularly in economic data, where small fluctuations can significantly impact predictions and decisions. The importance of local sensitivity for machine learning is also discussed, with suggestions for future applications in robust machine learning models, particularly in economic contexts. |
| action |
| ARLID |
cav_un_auth*0498693 |
| name |
International Joint Conference on Neural Networks 2025 (IJCNN 2025) |
| dates |
20250630 |
| mrcbC20-s |
20250705 |
| place |
Rome |
| country |
IT |
|
| RIV |
IN |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2026 |
| presentation_type |
PR |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0372672 |
| mrcbC61 |
1 |
| confidential |
S |
| arlyear |
2025 |
| mrcbU14 |
105023988800 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0642775 IJCNN 2025: International Joint Conference on Neural Networks Conference Proceedings IEEE 2025 Piscataway 979-8-3315-1042-8 |
|