09 March 2018


On Friday March 9, the Van Dantzig Seminar on statistics will host lectures by Jianqing Fan (Princeton University) and David Dunson (Duke University).

The program is (titles and abstracts below):

15.00 – 15.05 opening
15.05 – 16.05 Jianqing Fan
16.05 – 16.10 break
16.10 – 17.10 David Dunson
17.10 – drinks

Location: Snellius building, Leiden University, room 401. For directions and the university campus map see
Snellius in Leiden (https://www. locations/snellius-building# tab-3  )

Free attendance.

The Van Dantzig seminar is a nationwide series of lectures in statistics, that features renowned international and local speakers from the full breadth of the statistical sciences. The name honours David van Dantzig (1900-1959), who was the first modern statistician in the Netherlands, and professor in the “Theory of Collective Phenomena” (i.e. statistics) in Amsterdam. The seminar will convene 4 to 6 times a year at varying locations, and is financially supported by, among others, the STAR cluster and the Section Mathematical Statistics of the VVS-OR.

Everybody is cordially invited to attend.

Johannes Schmidt-Hieber and Frank van der Meulen


Jianqing Fan
Princeton University
Factor-Adjusted Robust Multiple Testing

Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for estimating the false discovery proportion (FDP) often ignore the effect of heavy-tailedness and the dependence structure among test statistics, and thus may lead to inefficient or even inconsistent estimation.  Also, the assumption of joint normality is often imposed, which is too stringent for many applications.  To address these challenges, in this paper we propose a factor-adjusted robust procedure for large-scale simultaneous inference with control of the false discovery proportion. We demonstrate that robust factor adjustments are extremely important in both improving the power of the tests and controlling FDP.  We identify  general conditions under which the proposed method produces consistent estimate of the FDP.  Extensive numerical experiments demonstrate the advantage of the proposed method over several state-of-the-art methods especially when the data generated from heavy-tailed distributions.  The method is convincingly illustrated by the German neuroblastoma trials.

(Based on joint work with Yuan Ke, Qiang Sun, and Wenxin Zhou)