Inconsistency of Resampling Algorithms for High-Breakdown Regression Estimators and a New Algorithm

By Douglas M. Hawkins, David J. Olive. 2002

99 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f2ba0fe03261266fa52f22b8611261823d2155da

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In Statistics and Probability

1. Mia Hubert
2. Peter J. Rousseeuw
3. S. Van Aelst
4. Andrea Cerioli
5. Anthony B. Atkinson
6. Marco Riani
7. Christophe Croux
8. Domenico Perrotta

Citations in the corpus

• High-Breakdown Robust Multivariate Methods (2008, methodology): “For more discussion on resampling algorithms, see Hawkins and Olive (2002).”
• The multivariate least-trimmed squares estimator (2008, background): “[10] for more discussion on the number of starting points in resampling algorithms.”
• Monitoring robust regression (2014, background): “Hawkins and Olive (2002), Point 3, recommend using several estimators, both classical and robust.”
• Robust linear regression: A review and comparison (2014, unknown): “, 2000; Hawkins and Olive, 2002). The commonly adopted method is to use the elemental resampling algorithm to obtain a number of subsets of data and calculate the initial regression estimate for each element set, and then compute the robust regression estimate from a number of initial estimates. How”