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Entropy Based Static Imputation

Entropy-Based Static Imputation

Context

  • Entropy-Based Static Imputation is based on Static 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 Static Imputation.

For a thorough introduction to this topic, please see Chapter 9: Missing Values Processing in our e-book.


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