Timeline for Can I remove outliers from a residual plot? Or does this compromise the validity of my model?

Current License: CC BY-SA 4.0

6 events

when toggle format	what		by	license	comment
Jan 21, 2021 at 11:16	vote	accept	jessirocha
Jan 20, 2021 at 18:11	comment	added	Deathkill14		The residuals are a reflection of how the model fits the data. It is possible that there is enough data and their "leverage" is so low that they are pretty much being ignored in the fit. However, it can also be that they are outliers in the residuals and have a big impact on the model fit. (Often outliers "mask" themselves so that they pull the fit to themselves and aren't residual outliers). If masking had occured the first time out fit the model and you eliminated points you may have been eliminating good ones. So indeed, an automatic method is preferred like the one in the link.
Jan 20, 2021 at 14:12	comment	added	jessirocha		Thank you Frank! I thought so, just wanted to be sure.
Jan 20, 2021 at 13:35	comment	added	Frank Harrell		The approach you are advocating is nothing more than data dredging and it invalidates the analysis. Pick a robust method that does not allow large influence from extreme observations and stick with the results.
Jan 20, 2021 at 13:26	comment	added	jessirocha		Thank you for your reply. I understand what you mean. But it seems like you are mentioning the approach of dealing with outliers in an initial stage. What was done here, however, it is to use a ARIMA model for prediction using all datapoints of the dataset as input. However, in the end, when it came to the evaluation part, a few datapoints from the residuals plot were considered too far away from the others, and thus, eliminated. So I wonder if your explanation is also valid when we are talking about eliminating only residuals datapoints when the model was created with every single datapoint.
Jan 20, 2021 at 13:11	history	answered	Deathkill14	CC BY-SA 4.0