On false discovery rate thresholding for classification under sparsity

Details On false discovery rate thresholding for classification under sparsity On false discovery rate thresholding for classification under sparsity

2011-06-30

arxiv.org/abs/1106.6147v1

Statistics

Methodology

Scientific paper

We study the properties of false discovery rate (FDR) thresholding, viewed as a classification procedure. The "0"-class (null) is assumed to have a known, symmetric log-concave density while the "1"-class (alternative) is obtained from the "0"-class either by translation (location model) or by scaling (scale model). Furthermore, the "1"-class is assumed to have a small number of elements w.r.t. the "0"-class (sparsity). Non-asymptotic oracle inequalities are derived for the excess risk of FDR thresholding. In a regime where Bayes power is away from 0 and 1, these inequalities lead to explicit rates of convergence of the excess risk to zero. Moreover, these theoretical investigations suggest an explicit choice for the nominal level $\alpha_m$ of FDR thresholding, in function of $m$. Our oracle inequalities show theoretically that the resulting FDR thresholding adapts to the unknown sparsity regime contained in the data. This property is illustrated with numerical experiments, which show that the proposed choice of $\alpha_m$ is relevant for a practical use.

Affiliated with

Also associated with

No associations

Rating

On false discovery rate thresholding for classification under sparsity does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with On false discovery rate thresholding for classification under sparsity, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On false discovery rate thresholding for classification under sparsity will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-34798