The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Efficient Bounded-Influence Regression Estimation
Abstract
The least squares estimator for β in the classical linear regression model is strongly efficient under certain conditions. However, in the presence of heavy-tailed errors and/or anomalous data, the least squares efficiency can be markedly reduced. In this article we propose an estimator that limits the influence of any small subset of the data and show that it satisfies a first-order condition for strong efficiency subject to the constraint. We then show that the estimator is asymptotically normal. The article concludes with an outline of an algorithm for computing a bounded-influence regression estimator and with an example comparing least squares, robust regression as developed by Huber, and the estimator proposed in this article.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Regression Diagnostics: Identifying Influential Data and Sources of Collinearity
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980) · Wiley Series in Probability and Statistics · doi:10.1002/0471725153 · Cited by 7,297
Self-citationHuber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
Robust regression using iteratively reweighted least-squares.
Holland, P. W., & Welsch, R. E. (1977) · Communications in Statistics - Theory and Methods · doi:10.1080/03610927708827533 · Cited by 2,115
Self-citation
Cited by
Robust Statistics: The Approach Based on Influence Functions
Ruppert, D. (1987) · Technometrics · doi:10.2307/1269782 · Cited by 3,880
Income distribution, political instability, and investment
Alesina, A., & Perotti, R. (1996) · European Economic Review · doi:10.1016/0014-2921(95)00030-5 · Cited by 2,303
Growth, income distribution, and democracy: What the data say
Perotti, R. (1996) · Journal of Economic Growth · doi:10.1007/bf00138861 · Cited by 2,051
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
Resource abundance and economic growth in the United States
Papyrakis, E., & Gerlagh, R. (2007) · European Economic Review · doi:10.1016/j.euroecorev.2006.04.001 · Cited by 542
Metadata
- Authors
- Krasker, W. S., & Welsch, R. E.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1982-09
- Volume
- 77
- Issue
- 379
- Pages
- 595-604
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1982182504
- Cited by
- 359
- References
- 3
- FWCI
- 25.11
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Efficient Bounded-Influence Regression Estimation. https://app.cochat.ai/papers/10.1080/01621459.1982.10477855
- The paper
- Krasker, W. S., & Welsch, R. E. (1982). Efficient Bounded-Influence Regression Estimation. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1982.10477855
Data sources
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.
