作者：Li, S.; Sesia, M.; Romano, Y.; Candes, E.; Sabatti, C.
作者单位：Stanford University; University of Southern California; Technion Israel Institute of Technology
摘要：In this article we develop a method based on model-X knockoffs to find conditional associations that are consistent across environments, while controlling the false discovery rate. The motivation for this problem is that large datasets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations replicated under different conditions may be more interesting. In fact, sometimes consist...
作者：Padilla, Oscar Hernan Madrid; Chatterjee, Sabyasachi
作者单位：University of California System; University of California Los Angeles; University of Illinois System; University of Illinois Urbana-Champaign
摘要：We study quantile trend filtering, a recently proposed method for nonparametric quantile regression, with the goal of generalizing existing risk bounds for the usual trend-filtering estimators that perform mean regression. We study both the penalized and the constrained versions, of order r >= 1, of univariate quantile trend filtering. Our results show that both the constrained and the penalized versions of order r >= 1 attain the minimax rate up to logarithmic factors, when the (r - 1)th disc...
