Robust estimation in very small samples

By Peter J. Rousseeuw, Sabine Verboben. 2002

129 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/132f04afad1afac2002b833f0cf85127720a6864

How researchers see this paper

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In Statistics and Probability

1. Christine H. Müller
2. Alejandro Cesar Orgambide Frery
3. Héctor Allende
4. Ivan Mizera
5. Robert Duchnowski
6. Richard J. Samworth
7. Chao Gao
8. Roger W. Koenker

Citations in the corpus

• Location–Scale Depth (2004, background): “For more discussion on estimating location and scale in very small datasets, see Hoaglin, Mosteller, and Tukey (1983) and Rousseeuw and Verboven (2002).”
• M-estimators with asymmetric influence functions: the distribution case (2006, background): “When we work with symmetric distributions, their SEIF are symmetric too; because of this reason, typically symmetric robustifying functions are selected [19].”
• Hodges–Lehmann estimates in deformation analyses (2013, methodology): “This application results from high robustness of R-estimates against outliers (see, Rousseeuw and Verboven 2002; Duchnowski 2011) and in some cases can give better results than conventional methods, for example, when two of four possible reference points are unstable (Duchnowski 2010).”