You're faced with a dataset where missing values are missing not at random (MNAR). What advanced imputation method would you choose and why?
- Mean imputation, as it's simple
- Model-based method, as it can model the missing data mechanism
- Multiple imputation, as it can handle large data
- Regression imputation, as it considers relationships
For data missing not at random (MNAR), a model-based method is preferable, as it allows for the explicit modeling of the missing data mechanism. This enables the handling of the systematic pattern in the missingness, reducing bias in the imputed values.
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