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UID:512@lincs.fr
DTSTART;TZID=Europe/Paris:20191127T103000
DTEND;TZID=Europe/Paris:20191127T120000
DTSTAMP:20191125T114721Z
URL:https://www.lincs.fr/events/false-discovery-rate/
SUMMARY:False Discovery Rate
DESCRIPTION:The false discovery rate (FDR) is a statistical approach used
 in multiple hypothesis testing to correct for multiple comparisons. It is
 typically used in high-throughput experiments in order to correct for
 random events that falsely appear significant. When testing a null
 hypothesis to determine whether an observed score is statistically
 significant\, a measure of confidence\, the p-value\, is calculated and
 compared to a confidence threshold ?. When k hypotheses are tested
 simultaneously with a confidence level ?\, the chances of occurrence of
 false positives (i.e.\, rejecting the null hypothesis when in fact it is
 true) is equal to 1 ? (1 ? ?)k\, which can lead to a high error rate in the
 experiment. Therefore\, a multiple testing correction\, such as the FDR\,
 is needed to adjust our statistical confidence measures based on the number
 of tests performed.\n\nReferences: False Discovery Rate
 (https://doi.org/10.1007/978-1-4419-9863-7_223)\, Computer Age Statistical
 Inference (By Bradley Efron\, Trevor Hastie).
CATEGORIES:Network Theory,Working Group
LOCATION:Doctoral Training Center (EIT Digital)\, 23\, avenue d'Italie\,
 Paris\, 75013\, France
GEO:48.8283983;2.3568972000000485
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=23\, avenue d'Italie\,
 Paris\, 75013\, France;X-APPLE-RADIUS=100;X-TITLE=Doctoral Training Center
 (EIT Digital):geo:48.8283983,2.3568972000000485
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TZID:Europe/Paris
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DTSTART:20191027T020000
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