Timeline for How to quantify the effect of outliers when estimating a regression coefficient?
Current License: CC BY-SA 4.0
4 events
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| Jan 22, 2023 at 21:52 | history | edited | utobi | CC BY-SA 4.0 |
fixed broken links
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| Mar 1, 2019 at 21:30 | comment | added | AdamO |
There is a close connection between the influence function and the df-beta: dfbeta is automatically in R as part of the stats package. This deletion diagnostic gives the relative change in magnitude of the regression coefficient from deleting the $i$-th observation for all $n$ observations in an lm or glm model. It's also closely connected with the jackknife robust error estimator.
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| Dec 27, 2016 at 21:42 | history | edited | Michael R. Chernick | CC BY-SA 3.0 |
added 371 characters in body
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| Dec 27, 2016 at 21:15 | history | answered | Michael R. Chernick | CC BY-SA 3.0 |