Local Sensitivity Analysis of Highly Robust Regression Estimators

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00642816" target="_blank" >RIV/67985556:_____/25:00642816 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/25:00642774

Výsledek na webu

<a href="http://dx.doi.org/10.1109/IJCNN64981.2025.11227341" target="_blank" >http://dx.doi.org/10.1109/IJCNN64981.2025.11227341</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN64981.2025.11227341" target="_blank" >10.1109/IJCNN64981.2025.11227341</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Local Sensitivity Analysis of Highly Robust Regression Estimators

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Local Sensitivity Analysis of Highly Robust Regression Estimators

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IJCNN 2025: International Joint Conference on Neural Networks Conference Proceedings

ISBN

979-8-3315-1042-8

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Rome

Datum konání akce

30. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—