Entropy-Based Dynamic Imputation

Context

• Entropy-Based Dynamic Imputation is based on Dynamic Imputation.
• Whereas the standard, non-entropy-based approaches randomly choose the sequence in which missing values are imputed within a row of data, the entropy-based methods select the order based on the conditional uncertainty associated with the unobserved variable.
• More specifically, missing values are imputed first for those variables that meet the following conditions:
  • Variables that have a fully-observed/imputed Markov Blanket.
  • Variables that have the lowest conditional Entropy, given the observations and imputed values.
• The advantages of the entropy-based methods are:
  • The speed improvement over their corresponding standard methods.
  • Their improved ability to handle datasets with large proportions of missing values.

Usage

• Select Menu > Learning > Missing Values Processing > Entropy-Based Dynamic Imputation.