The DOI matches its Crossref record.
Outlier Detection
Abstract
Outliers are a serious concern because modern outlier detection methods suggest that they are relatively common, can distort commonly used summaries of data, and can result in relatively low efficiency and low power when using methods based on means or least squares regression. Methods for dealing with outliers, in the context of estimation and hypothesis testing, are outlined.
Overview
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Quantitative outliers were identified and adjusted by winsorising [59].”
From What People Really Think About Safety around Horses: The Relationship between Risk Perception, Values and Safety Behaviours (2020) · cites this paper as [59]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 4
- Barnett V., 1994, Outliers in Statistical Data
- 10.1002/0471725382 · DOI · Paper page
- Wilcox R. R., 2003, Applying Contemporary Statistical Techniques
- Wilcox R. R., 2004, Introduction to Robust Estimation and Hypothesis Testing
Cited by
Sorted by citations
Chapman, M., Thomas, M., & Thompson, K. (2020) · Animals · doi:10.3390/ani10122222 · Cited by 20
Metadata
- Authors
- Wilcox, R. R.
- Venue
- Encyclopedia of Statistics in Behavioral Science
- Publisher
- Wiley
- Published
- 2005-04-15
- Cited by
- 3
- References
- 0
- Language
- en
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.
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