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Survey on Evaluating Missing Values

In the present communication; concept methods of replacing missing data has been reviewed and discussed. This article reviews the key ideas of multiple imputations and data augmentations, beside we review some usual methods. Until recently, the only missing-data methods available to most data analysts have been relatively ad hoc practices such as list-wise deletation. We want you to go more familiar to recent dramatic advances in theorical and computational statistics, which have produced a new generation of flexible procedures with a sound statistical basis. These procedures involve multiple imputation (Rubin, 1987), a simulation technique that replaces each missing datum with a set of m>l plausible values. Them versions of the complete data are analyzed by standard complete data methods, and the results are combined using simple rules to yield estimates and the standard errors that formally incorporate missing-data uncertainty.