Regression Neural Networks with a Highly Robust Loss Function

DOI10.1007/978-3-030-48814-7_2zbMath1455.62109OpenAlexW3043093418MaRDI QIDQ5141227

Petra Vidnerová, Jan Kalina

Publication date: 18 December 2020

Published in: Analytical Methods in Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-48814-7_2

zbMATH Keywords

robustnessneural networksnonlinear regression

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Hypothesis testing in multivariate analysis (62H15) General nonlinear regression (62J02) Neural nets and related approaches to inference from stochastic processes (62M45)

Uses Software

• R
• UCI-ml
• Keras
• GitHub
• Apache Spark

Cites Work

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• Reweighted least trimmed squares: an alternative to one-step estimators
• Semiparametrically weighted robust estimation of regression models
• The minimum weighted covariance determinant estimator
• High-breakdown robust multivariate methods
• Implicitly weighted methods in robust image analysis
• Neural Networks and Statistical Learning
• Robust Statistical Methods with R
• Radial basis function networks 1. Recent developments in theory and applications

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