  Modern biotechnologies often result in high-dimensional data sets with much
more variables than observations (n $\ll$ p). These data sets pose new
challenges to statistical analysis: Variable selection becomes one of the most
important tasks in this setting. We assess the recently proposed flexible
framework for variable selection called stability selection. By the use of
resampling procedures, stability selection adds a finite sample error control
to high-dimensional variable selection procedures such as Lasso or boosting. We
consider the combination of boosting and stability selection and present
results from a detailed simulation study that provides insights into the
usefulness of this combination. Limitations are discussed and guidance on the
specification and tuning of stability selection is given. The interpretation of
the used error bounds is elaborated and insights for practical data analysis
are given. The results will be used to detect differentially expressed
phenotype measurements in patients with autism spectrum disorders. All methods
are implemented in the freely available R package stabs.
