missing data - Test set imputation - Cross Validated As far as the second point - people developing predictive models rarely think how missing data occurs in application You need to have methods for missing values to render useful predictions - this is a "so called package deal" It seems hard to make a case that you can observe the future "test" set in batch and re-develop an imputation model
Why is multiple imputation not used more widely in Data Science . . . Multiple imputation is very commonly used to handle missing data, and if it is not used it almost always results in serious criticism Recently I have been interviewing for data scientist roles and so far none of the employers appear to use multiple imputation A quick search for "multiple imputation" on this site resulted in only 27 matches !
Multiple Imputation method in RCT - Cross Validated We decided to use the multiple imputation method in a RCT to solve the problem of some follow-up missing data (for completely random reasons) I was planning on using the Multiple Imputation method
Imputation by regression in R - Cross Validated The imputation that is conducted based on this filled data is completely deterministic If you want to keep the starting data fixed, you can use the argument data init
Best way to combine MCMC inference with multiple imputation? Multiple imputation is one way of handling uncertainty due to imputation That's why I'm wondering if there is a principled way to combined multiple imputation with procedures like MCMC so that my posterior estimates account for the imputation uncertainty Thanks!
Multiple imputation for outcome variables - Cross Validated Imputation itself adds uncertainty, for which reason multiple imputation is recommended, which basically explores, based on a range of seemingly "realistic" imputation values, how much uncertainty comes from the imputation (We should also have in mind that the real uncertainty is even larger, because the imputation model itself is uncertain )
How to decide whether missing values are MAR, MCAR, or MNAR Here you can use the simplest imputation methods or if feasible remove the data but you can never prove data is MCAR Rather you have to show it is unlikely it is MAR or MNAR MAR Is not what it sounds (Missing at random), it only means data is missing randomly related to the value of the observation but NOT randomly as related to other variables