Benchmark testing of algorithms for very robust regression: FS, LMS and LTS

DOI10.1016/j.csda.2012.02.003zbMath1252.62033OpenAlexW1968845600MaRDI QIDQ693263

Anthony C. Atkinson, Marco Riani, Domenico Perrotta, Francesca Torti

Publication date: 7 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.02.003

zbMATH Keywords

maskingforward searchleast trimmed squaresoutlier detectioncombinatorial searchleast median of squaresconcentration steplogistic plots of power

Mathematics Subject Classification ID

Related Items (10)

(Psycho-)analysis of benchmark experiments: a formal framework for investigating the relationship between data sets and learning algorithms ⋮ SOCP relaxation bounds for the optimal subset selection problem applied to robust linear regression ⋮ A practical approximation algorithm for the LTS estimator ⋮ Multivariate elliptical truncated moments ⋮ Robust regression analysis: a useful two stage procedure ⋮ An impartial trimming algorithm for robust circle fitting ⋮ Monitoring robust regression ⋮ Benchmark testing of algorithms for very robust regression: FS, LMS and LTS ⋮ Fast algorithms for the minimum volume estimator ⋮ A parametric framework for the comparison of methods of very robust regression

Uses Software

• MersenneTwister
• robustbase
• LIBRA

Cites Work

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