Title: Active predictor detection by controlling the false discovery rate
Authors: Yuanyuan Lin - The Chinese University of Hong Kong (Hong Kong) [presenting]
Wenlu Tang - The Chinese University of Hong Kong (China)
Jinhan Xie - Yunnan University (China)
Abstract: In modern scientific discoveries, important variables identification in analyzing high dimensional data is intrinsically challenging, especially when there are complex relationships among predictors. Without any specification of a regression model, we introduce an association statistic based on quantiles to identify influential predictors, which is flexible to capture a wide range of dependence. The asymptotic null distribution of the proposed statistic is established under mild conditions. Moreover, a multiple testing procedure is advocated to simultaneously test the independence between each predictor and the response variable in ultra-high dimensionality. It is computationally efficient as no optimization or resampling is involved. We prove its theoretical properties rigorously and justify the proposal asymptotically controls the false discovery rate. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.