Timeline for Removing outliers in logistic regression
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Mar 14, 2019 at 19:48 | comment | added | user54285 | There is broad disagreement among the authors I have read on the best way to test for outliers and whether you should remove them or not [or transform them which is often preferred to removal]. If you are going to conduct analysis with the full data set I think that is the data set you should search for outliers with, although I have not seen this point addressed. When you find one you should try to figure out why it is occurring which will improve your model. | |
| Mar 14, 2019 at 18:31 | comment | added | whuber♦ | The implicit assumption--that removing outliers and (presumably) refitting the model will make it more accurate--is doubtful and probably not correct in general. Perhaps your question would be better formulated as "does how one treat observations with outlying deviance residuals affect the accuracy of a logistic regression model and, if so, how should that be done?" | |
| Mar 14, 2019 at 12:15 | review | First posts | |||
| Mar 14, 2019 at 12:24 | |||||
| Mar 14, 2019 at 12:10 | history | asked | Vikrant Arora | CC BY-SA 4.0 |