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Activity Number: 582
Type: Invited
Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #310943
Title: Marginal Screening and Outperforming Bonferroni
Author(s): Ian Wray McKeague*+ and Min Qian
Companies: Columbia University and Columbia University
Keywords: marginal screening ; asymptotics ; non-regularity ; high-dimensional ; bootstrap ; post-model selection
Abstract:

This talk discusses marginal screening for detecting the presence of significant predictors in high-dimensional regression. Screening large numbers of predictors is a challenging problem due to the non-standard limiting behavior of post-model-selected estimators. There is a common misconception that the oracle property for such estimators is a panacea, but the oracle property only holds away from the null hypothesis of interest in marginal screening. To address this difficulty, we propose an adaptive resampling test (ART). Our approach provides an alternative to the popular (yet conservative) Bonferroni method of controlling familywise error rates. ART is adaptive in the sense that thresholding is used to decide whether the centered percentile bootstrap applies, and otherwise adapts to the non-standard asymptotics in the tightest way possible.


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