Weighted Nearest Neighbor Imputation of Missing Values using Selected Variables
Introduce MCAR Missing Values in a matrix for cross validation
Introduce MCAR Missing Values in a matrix
Mean Absolute Imputation Error
Mean Squared Imputation Error
Normalized Root Mean Squared Imputatoin Error
Cross Validation for wNNSel Imputation
Weighted Nearest Neighbor Imputation of Missing Values using Selected ...
Weighted Nearest Neighbor Imputation of Missing Values using Selected ...
Imputatin using wNNSel method.
New tools for the imputation of missing values in high-dimensional data are introduced using the non-parametric nearest neighbor methods. It includes weighted nearest neighbor imputation methods that use specific distances for selected variables. It includes an automatic procedure of cross validation and does not require prespecified values of the tuning parameters. It can be used to impute missing values in high-dimensional data when the sample size is smaller than the number of predictors. For more information see Faisal and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.