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Robust statistics are statistics that maintain their properties even if the underlying distributional assumptions are incorrect. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers.…
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outliers data robust displaystyle mean estimator influence function distribution point breakdown example statistics one standard values methods trimmed m-estimators model
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| longtailedness are also resistant to the presence of outliers | instance of | more robust estimators that are not so sensitive to distributional distortions | 0.80 | text |
| Student's t-distribution.For the t-distribution with ν | instance of | usually deal with heavy-tailed distributions | 0.80 | text |
| Robust statistics | related to External links | Peter Rousseeuw's | 0.60 | section |
| Robust statistics | related to External links | Brian Ripley's | 0.60 | section |
| Robust statistics | related to External links | Nick Fieller's | 0.60 | section |
| Robust statistics | related to External links | Statistical Modelling | 0.60 | section |
| Robust statistics | related to External links | Computation Archived | 0.60 | section |
| Robust statistics | related to External links | Wayback Machine | 0.60 | section |
| Robust statistics | related to External links | David Olive's | 0.60 | section |
| Robust statistics | related to External links | Online | 0.60 | section |
| Robust statistics | related to External links | JSXGraph | 0.60 | section |
| Robust statistics | related to Introduction | Robust | 0.60 | section |
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