Under what conditions might a model-based method be preferred over other imputation methods?
- When a known and well-fitting model can be assumed for the data
- When the amount of missing data is negligible
- When the data is missing completely at random
- When the data is not missing at random
A model-based method might be preferred over other imputation methods when a known and well-fitting model can be assumed for the data. The model-based method is a principled method of handling missing data under the assumption that the data follows a specific statistical model. It could be any model like linear regression, logistic regression, etc.
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