Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Distribution-free Multiple Testing

Ery Arias-Castro
University of California, San Diego
Room 306, Statistics Building 1130

We study a stylized multiple testing problem where the test statistics are independent and assumed to have the same distribution under their respective null hypotheses. We first show that, in the normal means model where the test statistics are normal Z-scores, the well-known method of (Benjamini and Hochberg, 1995) is optimal in some asymptotic sense. We then show that this is also the case of a recent distribution-free method proposed by Foygel-Barber and Candes (2015). The method is distribution-free in the sense that it is agnostic to the null distribution — it only requires that the null distribution be symmetric. We extend these optimality results to other location models with a base distribution having fast-decaying tails.

 

This is a joint research with Shiyun Chen. The corresponding paper is available online at https://arxiv.org/abs/1604.07520

http://www.math.ucsd.edu/~eariasca/

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.