Online Program Home
  My Program

Abstract Details

Activity Number: 465 - Biometrics and High-Dimensional Data
Type: Contributed
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #322830
Title: Data-Adaptive Statistics for Multiple Hypothesis Testing in High-Dimensional Settings
Author(s): Weixin Cai* and Alan Hubbard
Companies: University of California, Berkeley and University of California, Berkeley
Keywords: data mining ; data-adaptive statistical target parameter ; cross-validation ; machine learning ; targeted maximum likelihood estimation
Abstract:

Current statistical inference problems in areas like astronomy, genomics, and marketing routinely involve the simultaneous testing of thousands -even millions- of null hypotheses. For high-dimensional multivariate distributions, these hypotheses may concern a wide range of parameters, with complex and unknown dependence structures among variables. In analyzing such hypothesis testing procedures, gains in efficiency and power can be achieved by performing variable reduction on the set of hypotheses prior to testing. We present in this paper an approach using data-adaptive multiple testing that serves exactly this purpose. This approach applies data mining techniques to screen the full set of covariates on equally sized partitions of the whole sample via cross-validation.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association