From robust neural networks toward robust nonlinear quantile estimation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F25%3A00642808" target="_blank" >RIV/67985556:_____/25:00642808 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/25:00635825

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/07474946.2025.2498933" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/07474946.2025.2498933</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/07474946.2025.2498933" target="_blank" >10.1080/07474946.2025.2498933</a>

Jazyk výsledku

angličtina

Název v původním jazyce

From robust neural networks toward robust nonlinear quantile estimation

Popis výsledku v původním jazyce

Regression quantiles provide a flexible framework for modeling the conditional distribution of a response variable by estimating different parts of its distribution, thereby offering valuable insights into the relationship between predictors and outcomes. However, existing nonlinear regression quantile methods may be sensitive to the presence of severe outliers in the data. This paper starts with investigating robust versions of neural networks. The study includes a proposal of a sequential outlier detection procedure based on sequential example selection for robust neural networks. Further, robust quantile estimators for nonlinear regression is introduced. The proposed quantiles are inspired by least weighted squares regression. To enhance robustness to outliers, they assign implicit weights to individual samples and are specifically tailored for multilayer perceptrons, radial basis function networks, and regularized networks. Numerical experiments demonstrate that the robust quantiles improve generalization and outlier resistance. Simulations confirm that the proposed method outperforms traditional (non-robust) quantiles.

Název v anglickém jazyce

From robust neural networks toward robust nonlinear quantile estimation

Popis výsledku anglicky

Regression quantiles provide a flexible framework for modeling the conditional distribution of a response variable by estimating different parts of its distribution, thereby offering valuable insights into the relationship between predictors and outcomes. However, existing nonlinear regression quantile methods may be sensitive to the presence of severe outliers in the data. This paper starts with investigating robust versions of neural networks. The study includes a proposal of a sequential outlier detection procedure based on sequential example selection for robust neural networks. Further, robust quantile estimators for nonlinear regression is introduced. The proposed quantiles are inspired by least weighted squares regression. To enhance robustness to outliers, they assign implicit weights to individual samples and are specifically tailored for multilayer perceptrons, radial basis function networks, and regularized networks. Numerical experiments demonstrate that the robust quantiles improve generalization and outlier resistance. Simulations confirm that the proposed method outperforms traditional (non-robust) quantiles.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA24-10078S" target="_blank" >GA24-10078S: Nové neparametrické nástroje pro ekonometrickou analýzu dat</a><br>

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 periodika

Sequential Analysis

ISSN

0747-4946

e-ISSN

1532-4176

Svazek periodika

44

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

26

Strana od-do

350-326

Kód UT WoS článku

001489380800001

EID výsledku v databázi Scopus

2-s2.0-105005517502