Integrated Path Stability Selection

Feature/variable selection is a fundamental technique in data science that aims to identify the relevant features in a dataset. A critical component of feature selection is false positive control. Several popular methods---including the classical Benjamini-Hochberg procedure, stability selection, and, most recently, knockoffs---address this challenge, but often at the expense of identifying few true positives. In this talk, I will introduce a new version of stability selection, integrated path stability selection (IPSS),that yields significantly more true positives in practice than existing methods while still controlling false positives at desired rates. Furthermore, IPSS is computationally efficient, easy to implement, and effective in high dimensions. It also offers parametric and nonparametric versions, with the latter capable of capturing nonlinear relationships in data. After introducing the method, I will demonstrate its performance with applications to cancer data