Statistical Imputation

A Statistical Imputation is a Statistical Task of replacing missing data with substitued values

• AKA: Imputation.
• Context:
  • It can range from being an Unit Imputation to being an Item Imputation.
• Example(s):
  • Hot Deck Imputation,
  • Cold Deck Imputation,
  • Listwise Deletion,
  • Pairwise Deletion,
  • Mean Imputation,
  • Regression Imputation,
  • Last-Observation-Carried-Forward (LOCF),
  • Stochastic Imputation,
  • Multiple Imputation.
  • …
• Counter-Example(s):
  • Interpolation,
  • Extrapolation.
• See: Statistical Bias, Statistics, Missing Data, List of Statistical Packages.

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References

2018

• (Wikipedia, 2018) ⇒ https://en.wikipedia.org/wiki/Imputation_(statistics) Retrieved:2018-6-6.
  • In statistics, imputation is the process of replacing missing data with substituted values. When substituting for a data point, it is known as "unit imputation"; when substituting for a component of a data point, it is known as "item imputation". There are three main problems that missing data causes: missing data can introduce a substantial amount of bias, make the handling and analysis of the data more arduous, and create reductions in efficiency. Because missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with listwise deletion of cases that have missing values. That is to say, when one or more values are missing for a case, most statistical packages default to discarding any case that has a missing value, which may introduce bias or affect the representativeness of the results. Imputation preserves all cases by replacing missing data with an estimated value based on other available information. Once all missing values have been imputed, the data set can then be analysed using standard techniques for complete data. Imputation theory is constantly developing and thus requires consistent attention to new information regarding the subject. There have been many theories embraced by scientists to account for missing data but the majority of them introduce large amounts of bias. A few of the well known attempts to deal with missing data include: hot deck and cold deck imputation; listwise and pairwise deletion; mean imputation; regression imputation; last observation carried forward; stochastic imputation; and multiple imputation.

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