Multiple Imputation for Missing Data: Concepts and New ...

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Multiple Imputation for Missing Data: Concepts and NewDevelopment (Version )Yang C. Yuan, SAS Institute Inc., Rockville, MDAbstractMultiple Imputation provides a useful strategy for dealingwith data sets with Missing values. Instead of filling in asingle value for each Missing value, Rubin s (1987) multipleimputation procedure replaces each Missing value with aset of plausible values that represent the uncertainty aboutthe right value to impute. These multiply imputed data setsare then analyzed by using standard procedures for com-plete data and combining the results from these matter which complete-data analysis is used, the pro-cess of combining results from different imputed data setsis essentially the same. This results in valid statistical in-ferences that properly reflect the uncertainty due to paper reviews methods for analyzing Missing data, in-cluding basic Concepts and applications of Multiple impu-tation techniques.

nj−k−1 random variate and n j is the number of nonmissing observations for Y j. The regression coeffi-cients are drawn as β ∗ = βˆ+ σ ∗jV 0 hjZ where V0 hj is the upper triangular matrix in the Cholesky decomposition, V j = V0 hj V hj, and Z is a vector of k + 1 independent random normal variates. 2. The missing values are then ...

  Multiple, Imputation, Multiple imputation, Variate