The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Robust regression using repeated medians
Abstract
The repeated median algorithm is a robustified U-statistic in which nested medians replace the single mean. Unlike many generalizations of the univariate median, repeated median estimates maintain the high 50% breakdown value and can resist the effects of outliers even when they comprise nearly half of the data. For bivariate linear regression with symmetric errors, repeated median estimates are unbiased and Fisher consistent, and their efficiency under Gaussian sampling can be comparable to the efficiency of the univariate median.
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Cites
The Art of Computer Programming
Knuth, D. E. (1968) · Cited by 15,999
Estimates of the Regression Coefficient Based on Kendall's Tau
Sen, P. K. (1968) · Journal of the American Statistical Association · doi:10.1080/01621459.1968.10480934 · Cited by 14,517
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The Art of Computer Programming: Volume 3: Sorting and Searching
Knuth, D. E. (1973) · Cited by 3,955
A Class of Statistics with Asymptotically Normal Distribution
Hoeffding, W. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-0919-5_20 · Cited by 1,851
A General Qualitative Definition of Robustness
Hampel, F. R. (1971) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177693054 · Cited by 1,002
A Robust Method for Multiple Linear Regression
Andrews, D. F. (1974) · Technometrics · doi:10.1080/00401706.1974.10489233 · Cited by 635
A Robust Comparison of Biological Shapes
Siegel, A. F., & Benson, R. H. (1982) · Biometrics · doi:10.2307/2530448 · Cited by 204
Self-citationOn Median Tests for Linear Hypotheses
Brown, G. W., & Mood, A. M. (1951) · Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability · doi:10.1525/9780520411586-013 · Cited by 178
Efficiency in normal samples and tolerance of extreme values for some estimates of location
Hodges, J. L. (1967) · Project Euclid (Cornell University) · Cited by 99
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Extensions of the procrustes method for the optimal superimposition of landmarks
Rohlf, F. J., & Slice, D. (1990) · Systematic Zoology · doi:10.2307/2992207 · Cited by 4,289
High Breakdown-Point and High Efficiency Robust Estimates for Regression
Yohai, V. J. (1987) · The Annals of Statistics · doi:10.1214/aos/1176350366 · Cited by 1,644
USAC: A Universal Framework for Random Sample Consensus
Raguram, R., Chum, O., Pollefeys, M., Matas, J., & Frahm, J.-M. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.257 · Cited by 676
Robust regression methods for computer vision: A review
Meer, P., Mintz, D., Rosenfeld, A., & Kim, D. Y. (1991) · International Journal of Computer Vision · doi:10.1007/bf00127126 · Cited by 638
Freshwater salinization syndrome on a continental scale
Kaushal, S. S., Likens, G. E., Pace, M. L., Utz, R. M., Haq, S., Gorman, J., & Grese, M. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711234115 · Cited by 594
Metadata
- Authors
- SIEGEL, A. F.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1982
- Volume
- 69
- Issue
- 1
- Pages
- 242-244
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2144643813
- Cited by
- 567
- References
- 10
- FWCI
- 8.05
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
