Non-sparseϵ-insensitive support vector regression for outlier detection

DOI10.1080/02664763.2015.1005064OpenAlexW1974341989MaRDI QIDQ5130288

Sohel Rana, Habshah Midi, Waleed Dhhan

Publication date: 4 November 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2015.1005064

zbMATH Keywords

outliersrobustnessstatistical learning theorysupport vector regressionsparseness

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

The single-index support vector regression model to address the problem of high dimensionality ⋮ Detection of outliers in high-dimensional data using nu-support vector regression ⋮ Outlier detection in statistical modeling via multivariate adaptive regression splines ⋮ A high breakdown, high efficiency and bounded influence modified GM estimator based on support vector regression

Uses Software

• rattle
• glmnet

Cites Work

• Weighted least squares support vector machines: robustness and sparse approximation
• Outliers: A Formal Approach
• Support vector regression based residual control charts
• Data Mining with Rattle and R
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