Controlling false discoveries in high-dimensional situations: Boosting with stability selection
This work addresses variable selection challenges in high-dimensional biotechnological data, such as in autism spectrum disorder studies, offering an incremental improvement with error control.
The paper tackles the problem of false discoveries in high-dimensional data (n << p) by combining boosting with stability selection, showing through simulations that this approach provides finite sample error control and practical guidance for variable selection.
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.