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SUMMARY:Fusion and Individualized Fusion Learning from Diverse Data Source
 s by Confidence Distribution - Regina Liu (Rutgers\, The State University 
 of New Jersey)
DTSTART:20180322T133000Z
DTEND:20180322T143000Z
UID:TALK103180@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Inferences from different data sources can often be fused toge
 ther to yield more powerful findings than those from individual sources al
 one. We present a new approach for fusion learning by using the so-called 
 confidence distributions (CD). We further develop the individualized fusio
 n learning\, &lsquo\;iFusion&rsquo\;\, for drawing efficient individualize
 d inference by fusing the leanings from relevant data sources. This approa
 ch is robust for handling heterogeneity arising from diverse data sources\
 , and is ideally suited for goal-directed applications such as precision m
 edicine. In essence\, iFusion strategically &lsquo\;borrows strength&rsquo
 \; from relevant individuals to improve efficiency while retaining its inf
 erence validity. Computationally\, the fusion approach here is parallel in
  nature and scales up well in comparison with competing approaches. The pe
 rformance of the approach is demonstrated by simulation studies and risk v
 aluation&nbsp\; of aircraft landing data.  <br><br><br>
LOCATION:Seminar Room 1\, Newton Institute
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