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SUMMARY:Multiple testing procedures for large and complex dependent data t
 o study human brain complex network properties
DTSTART:20151026T150000
DTSTAMP:20260406T194742Z
UID:3df37d34736f2c556d1b613dca4e9f53261d195755a0226dd82aea0e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Djalel Meskaldji\, EPFL\nGiven the large number of papers writ
 ten over the last ten years on error controls in high dimensional multiple
  testing\, it would be worthwhile to consider a single comprehensive techn
 ique that allows user flexibility in error control. We describe a new and 
 comprehensive family of error rates (with a corresponding family of contro
 l) that contains and generalizes most existing proposals. It offers the sc
 ientist a broad choice on how to properly control for discovering false fi
 ndings. We also discuss the use of a particular choice that bridges the ga
 p between two well-known control error metrics: FWER and FDR. The second p
 art of the talk will be dedicated to how to use a screening and filtering 
 strategy to benefit from positive dependence to increase power of testing\
 , when data can be modeled as a complex network. As an illustration\, the 
 strategy is applied to compare topological differences between groups of h
 uman brain networks.\nBio: PhD. Applied mathematics and Electrical enginee
 ring\, EPFL\, 2013.\nDiploma\, Applied Mathematics\, EPFL\, 2009.
LOCATION:Campus Biotech\, H8-01 144.165
STATUS:CONFIRMED
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