In this paper, expectation maximization imputation based on the Gaussian mixture model (GMM-EM) and multiple imputation (MI) are respectively applied to perform missing data imputation for leaching process under different data loss rates and data loss patterns, and then the imputation performances are evaluated.
Using four empirical datasets, we evaluate and characterize four such imputation methods, referred to as k-nearest neighbors, singular value decomposition, random forest regression, and expectation maximization imputation, in terms of their imputation accuracies and the factors affecting accuracy.
The imputation methods compared were: mean imputation (MNI), k-nearest neighbors imputation (kNNI) (Troyanskaya et al. 2001), singular value decomposition imputation (SVDI) (Troyanskaya et al. 2001), expectation maximization imputation (EMI Dempsteret et al. 1977), and random forest regression imputation (RFI) (Stekhoven and Bühlmann 2011).
Impute: impute: Imputation for microarray data.
We used SPSS Missing Value Analysis MVAA), expectation maximization for imputation of missing values in respondents with valid data on at least half of the items within a measure.
We used SPSS Missing Value Analysis MVAA), expectation maximization (EM) for imputation of missing values in respondents with valid data for at least half of the items.
Not asked and not answered: multiple imputation for multiple surveys.
Schafer J. Estimation/multiple imputation for mixed categorical and continuous data.
Rubin DB (1987), Multiple Imputation for Nonresponse in Surveys, John Wiley.
Additional file 1 Multiple Imputation for Missing Data in RAD Data.
