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DTSTAMP:20250709T233739Z
DESCRIPTION:\n	Abstract:\n\n\nLarge scale multivariate regression with many 
 heavy-tailed responses arises in a wide range of areas from genomics\, fin
 ancial asset pricing\, banking regulation\, to psychology and social studi
 es. Simultaneously testing a large number of general linear hypotheses\, s
 uch as multiple contrasts\, based on the large scale multivariate regressi
 on reveals a variety of associations between responses and regression or e
 xperimental factors. Traditional multiple testing methods often ignore the
  effect of heavy-tailedness in the data and impose joint normality assumpt
 ion that is arguably stringent in applications. This results in unreliable
  conclusions due to the lose of control on the false discovery proportion/
 rate (FDP/FDR) and severe compromise of power in practice. In this paper\,
  we employ data-adaptive Huber regression to propose a framework of joint 
 robust inference of the general linear hypotheses for large scale multivar
 iate regression. With mild conditions\, we show that the proposed method p
 roduces consistent estimate of the FDP and FDR at a prespecified level. Pa
 rticularly\, we employ a bias-correction robust covariance estimator and s
 tudy its exponential-type deviation inequality to provide theoretical guar
 antee of our proposed multiple testing framework. Extensive numerical expe
 riments demonstrate the gain in power of the proposed method compared to O
 LS and other procedures.\n\n\n	Speaker\n\n\nWen Zhou is an Assistant Profes
 sor in the Department of Statistics at the Colorado State Unversity. His r
 esearch focuses on high dimensional data inference\, graphical modeling\, 
 statistical machine learning\, statistical genomics and bioinformatics\, s
 ystem biology\, optimization and game theory.\n
DTSTART:20191108T203000Z
DTEND:20191108T213000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Wen Zhou (Colorado State Unversity)
URL:/mathstat/channels/event/wen-zhou-colorado-state-u
 nversity-302242
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