AN INTEGRAL STUDY ON MISSING VALUE DATA IMPUTATION
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 0, Vol 5, Issue 2
Abstract
Imputation is the process of replacing missing data with substituted values. Missing data can create problems for analyzing data, imputation is seen as a way to avoid pitfalls involved with list wise deletion of cases that have missing values. That is to say, when one or more values are missing for a case affects the representativeness of the results [1]. Once all missing values have been imputed, the data set can then be analyzed using standard techniques for complete data. Many theories have been proposed for missing data computation but the majority of them introduce large amounts of bias. A very few techniques to deal with missing data include: hot deck and cold deck imputation; list wise and pair wise deletion; mean imputation; regression imputation; last observation carried forward; stochastic imputation; and multiple imputation. This paper reviews missing value data imputation methods for analyzing missing data, including basic concepts and applications of imputation techniques.
Authors and Affiliations
S. Swetha*
Keywords
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