Variable selection has been suggested for Random Forests to improve data prediction and interpretation. However, the basic element, i.e. variable importance measures, cannot be computed straightforward when there are missing values in the predictor variables. Possible solutions are multiple...

Random Forests are frequently applied as they achieve a high prediction accuracy and have the ability to identify informative variables. Several approaches for variable selection have been proposed to combine and intensify these qualities. An extensive review of the corresponding literature led...

The occurrence of missing data is a major problem in statistical data analysis. All scientific fields and data of all kinds and size are touched by this problem. There is a number of ad-hoc solutions which unfortunately lead to a loss of power, biased inference, underestimation of variability...