Technological advances and new demands due to economic and socio-cultural changes regularly challenge the National Statistical Institutes to adapt to their evolving environment. The application of machine learning methods as important and promising tools for official statistics are discussed in the context of these changes, in the context of opportunities arising from new digital data sources, and considering the difficult task of having to balance a variety of quality requirements at national and international level. Selective statistical data editing is an approach to detect influential units and select them for manual follow up in order to make the process more efficient. In this thesis, a simple and a two-step approach are developed to apply random forests to selective editing of continuous variables in the context of short-term business survey data. We present a score function based on decision forest models which allows for an efficient selection of units relevant for the estimation of the final estimates. The approach is found to be applicable also at the disaggregated levels of the autonomous communities and economic branches
