Data Preprocessing Questions
The common techniques used for missing data imputation are:
1. Mean/median imputation: This involves replacing missing values with the mean or median of the available data for that variable.
2. Last observation carried forward (LOCF): This method involves carrying forward the last observed value for a missing data point.
3. Multiple imputation: This technique involves creating multiple plausible imputations for missing values based on the observed data and using these imputations for subsequent analysis.
4. Regression imputation: This method involves using regression models to predict missing values based on the relationship between the variable with missing data and other variables.
5. Hot deck imputation: This technique involves randomly selecting a value from a similar record with complete data to impute the missing value.
6. K-nearest neighbors (KNN) imputation: This method involves finding the K most similar records with complete data and using their values to impute the missing value.
7. Expectation-maximization (EM) algorithm: This iterative algorithm estimates missing values by maximizing the likelihood of the observed data.
8. Multiple hot deck imputation: This technique combines hot deck imputation with multiple imputation to impute missing values.
It is important to note that the choice of imputation technique depends on the nature of the data, the amount of missingness, and the assumptions made about the missing data mechanism.