Timeline for Approach for multivariate outlier detection when treating missing values with FIML
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 28 at 2:48 | comment | added | jbowman | It doesn't really matter whether they do or not, since you aren't going to use them in the estimation procedure regardless. | |
| Feb 28 at 0:32 | comment | added | Madamadam | Let‘s assume, my dataset has 100 cases, DV is complete in all cases, but in 10 cases IV is missing. Then I can calculate mahalanobis distances for 90 cases but how do I know that the 10 values of the DV that don‘t have a corresponding IV value behave „outlier-ish“? Just doing an univariate outlier detection? | |
| Feb 28 at 0:14 | comment | added | Christian Geiser | FIML does not impute missing values. It simply uses all available data points. Therefore, I'm not sure I understand your question. That is, there would not be any imputed values that could count as "outliers." | |
| Feb 28 at 0:06 | history | asked | Madamadam | CC BY-SA 4.0 |