Controlling false discoveries in high-dimensional situations: Boosting with stability selection

TL;DR

This paper evaluates stability selection combined with boosting for variable selection in high-dimensional data, providing error control, practical guidance, and demonstrating its application in autism research.

Contribution

It introduces a detailed assessment of combining boosting with stability selection, including simulation results, error bounds interpretation, and practical implementation guidance.

Findings

Stability selection effectively controls false discoveries in high-dimensional settings.

The combination of boosting and stability selection improves variable selection accuracy.

Practical recommendations enhance the application of these methods in real data analysis.

Abstract

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.