A parametric framework for the comparison of methods of very robust regression

DOI10.1214/13-STS437zbMath1332.62245arXiv1405.5040MaRDI QIDQ254398

Marco Riani, Domenico Perrotta, Anthony C. Atkinson

Publication date: 8 March 2016

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1405.5040

zbMATH Keywords

distance of outliersforward searchleast trimmed squaresMM estimatemultiple outliersoverlap indexpoint contaminationregression diagnostics

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Robustness and adaptive procedures (parametric inference) (62F35)

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Robust methods for heteroskedastic regression, Multivariate elliptical truncated moments, Data driven robust estimation methods for fixed effects panel data models, Simulating mixtures of multivariate data with fixed cluster overlap in FSDA library, Reliable Robust Regression Diagnostics, Outlier-free merging of homogeneous groups of pre-classified observations under contamination, A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering, On Characterizations and Tests of Benford’s Law, Robust Bayesian regression with the forward search: theory and data analysis, Introducing Prior Information into the Forward Search for Regression, How to Marry Robustness and Applied Statistics, Monitoring robust regression, Benchmark testing of algorithms for very robust regression: FS, LMS and LTS, Objective Bayesian inference with proper scoring rules, The power of monitoring: how to make the most of a contaminated multivariate sample, A parametric framework for the comparison of methods of very robust regression

Uses Software

• robustbase
• FSDA

Cites Work

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