I have 5 methods for missing data imputation, since my original data set, has missing values due to the fact that is industrial data. And to perform a PCA analysis, and in order to have eigenvalues positives, I need a covariance to be determine positive.
I use the 5 methods to impute missing data, so now i got 5 new matrices of X_imputed.
Question: How can measure the performance of each one? what criteria should I use?
I read about calculation RMSE, but when I see the formula they use SQRT of Xi obs - Xi imputed, and they do the calculation because their initial X is complete, and they introduce a % of MD, but the problem for me is that i already start with Missing Data.