[Influential Observations, High Leverage Points, and Outliers in Linear Regression]: Comment
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Individual effects were identified as potentially influential if they had leverage (hat value above 2/n) or were discrepant (standardised residual of ±3).
“Individual effects were identified as potentially influential if they had leverage, defined by a hat value above 2/ n , a conservative cut-off (Hoaglin and Kempthorne 1986), or were discrepant, with a standardised residual of ± 3.”
From Effectiveness of Self-Compassion Related Therapies: a Systematic Review and Meta-analysis (2019) · cites this paper as Hoaglin and Kempthorne 1986
In “Identifying Influential Cases” (p. 1), the paper discusses a leverage cutoff, but gives it as 2p/N, not 2/n, and says it is neither the only nor necessarily the most useful rule of thumb. The citation is attached to the leverage cutoff; the sentence’s separate ±3 residual criterion is not assessed as attributed to this source. (LLM, conf=0.88)
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Research works printed but not linked: 7
- DAwsoN, R. (1985). Diagnosing data and prior influence in a Bayesian analysis. Unpublished Ph.D. thesis, Dept. Statistics, Harvard Univ.
- GRAY, J. B. (1983). The L − R plot: a graphical tool for assessing influence. In 1983 Proceedings of the Statistical Computing Section 159–164. Amer. Statist. Assoc., Washington, D. C.
- GRAY, J. B. (1985). Graphics for regression diagnostics. In 1985 Proceedings of the Statistical Computing Section 102–107. Amer. Statist. Assoc., Washington, D. C.
- HuBER, P. J. (1981). Robust Statistics. Wiley, New York.
- KEMPTHORNE, P. J. (1985). Decision-theoretic measures of influence in regression. In 1985 Proceedings of the Business and Economic Statistics Section 429-434. Amer. Statist. Assoc., Washington, D. C. To appear in J. Roy. Statist. Soc. Ser. B.
- KRASKER, W. S. and WELSCH, R. E. (1983). The use of boundedinfluence regression in data analysis: Theory, computation, and graphics. In Computer Science and Statistics: Proceedings of the 14th Symposium on the Interface (K. W. Heiner, et al., eds.) 45–51. Springer, New York.
- PETTIT, L. I. and SMITH, A. F. M. (1985). Outliers and influential observations in linear models. In Bayesian Statistics 2 (J. M. Bernardo, M. H. DeGroot, D. V. Lindley, and A. F. M. Smith, eds.) 473–494. North Holland, Amsterdam.
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- Authors
- Hoaglin, D. C., & Kempthorne, P. J.
- ORCID
- Hoaglin, David C.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1986-08-01
- Volume
- 1
- Issue
- 3
- Pages
- 408-412
- ISSN
- 0883-4237 (ISSN-L)
- 2168-8745
- OpenAlex
- W1970511008
- Cited by
- 24
- References
- 0
- Citations by year
- 2026: 2 · 2024: 2 · 2023: 3 · 2021: 2 · 2018: 2 · 2016: 1 · 2015: 2 · 2014: 3 · 2012: 1
- FWCI
- 0.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Statistical Science (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- influential observations, high leverage points, outliers, linear regression, regression diagnostics
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