Multiple Hypotheses Testing For Variable Selection
Statistics Theory
2012-06-12 v2 Methodology
Statistics Theory
Abstract
Many methods have been developed to estimate the set of relevant variables in a sparse linear model Y= XB+e where the dimension p of B can be much higher than the length n of Y. Here we propose two new methods based on multiple hypotheses testing, either for ordered or non-ordered variables. Our procedures are inspired by the testing procedure proposed by Baraud et al (2003). The new procedures are proved to be powerful under some conditions on the signal and their properties are non asymptotic. They gave better results in estimating the set of relevant variables than both the False Discovery Rate (FDR) and the Lasso, both in the common case (p<n) and in the high-dimensional case (p>n).
Cite
@article{arxiv.1106.3415,
title = {Multiple Hypotheses Testing For Variable Selection},
author = {Florian Rohart},
journal= {arXiv preprint arXiv:1106.3415},
year = {2012}
}