The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Robust Estimation of a Location Parameter
Abstract
This paper contains a new approach toward a theory of robust estimation; it treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators—intermediaries between sample mean and sample median—that are asymptotically most robust (in a sense to be specified) among all translation invariant estimators. For the general background, see Tukey (1960) (p. 448 ff.)
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Cites
Mathematical Methods of Statistics.
Geary, R. C., & Cramer, H. (1947) · The Economic Journal · doi:10.2307/2226151 · Cited by 5,927
A Class of Statistics with Asymptotically Normal Distribution
Hoeffding, W. (1948) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177730196 · Cited by 1,928
Estimates of Location Based on Rank Tests
Hodges, J. L., & Lehmann, E. L. (1963) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704172 · Cited by 1,260
Note on the Consistency of the Maximum Likelihood Estimate
Wald, A. (1949) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729952 · Cited by 1,100
Contributions to Probability and Statistics
(1989) · Springer eBooks · doi:10.1007/978-1-4612-3678-8 · Cited by 611
Anscombe, F. J. (1960) · Technometrics · doi:10.1080/00401706.1960.10489888 · Cited by 478
On some asymptotic properties of maximum likelihood estimates and related Bayes' estimates
Cam, L. L., & Lucien, M. (1953) · University of California Press eBooks · Cited by 388
Asymptotically Most Powerful Rank-Order Tests
Hajek, J. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704476 · Cited by 220
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Chicco, D., Warrens, M. J., & Jurman, G. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.623 · Cited by 5,380
Empirical Asset Pricing via Machine Learning
Gu, S., Kelly, B., & Xiu, D. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhaa009 · Cited by 2,545
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Distributional Reinforcement Learning With Quantile Regression
Dabney, W., Rowland, M., Bellemare, M., & Munos, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11791 · Cited by 612
Data collection and quality challenges in deep learning: a data-centric AI perspective
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Metadata
- Authors
- Huber, P. J.
- Venue
- Springer Series in Statistics
- Publisher
- Springer New York
- Published
- 1992
- Pages
- 492-518
- ISSN
- 0172-7397 (ISSN-L)
- OpenAlex
- W4249736682
- Cited by
- 1,455
- References
- 8
- FWCI
- 0.34
- Type
- book chapter
- Language
- en
- Open access
- closed
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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.