Marginal False Discovery Rates

Abstract

The author proposes a new perspective for defining false discovery rates in the content of penalized regression models. The mFDR is easy to compute and can control the number of noise features picked out by the model under some regularity conditions. When there are non-independent correlation structure among the noise features, the author proposes to use permutation method to estimate mFDR.