A Power and Prediction Analysis for Knockoffs with Lasso Statistics

Understand

Knockoffs is a new framework for controlling the false discovery rate (FDR) in multiple hypothesis testing problems involving complex statistical models.

• While there has been great emphasis on Type-I error control, Type-II errors have been far less studied.
• In this paper we analyze the false negative rate or, equivalently, the power of a knockoff procedure associated with the Lasso solution path under an i.i.d.
• Gaussian design, and find that knockoffs asymptotically achieve close to optimal power with respect to an omniscient oracle.