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UID:20250312T172032EDT-8285Id7jxU@132.216.98.100
DTSTAMP:20250312T212032Z
DESCRIPTION:Bayesian nonparametric modeling of heterogeneous groups of cens
 ored data.\n\nAnalysis of survival data arising from different groups\, wh
 ereby the data in each group is scarce\, but abundant overall\, is a commo
 n issue in applied statistics. Bayesian nonparametrics are tools of choice
  to handle such datasets given their ability to share information across g
 roups. In this presentation\, we will compare three popular Bayesian nonpa
 rametric methods on the modeling of survival functions coming from related
  heterogeneous groups. Specifically\, we will first compare the modeling a
 ccuracy of the Dirichlet process\, the hierarchical Dirichlet process\, an
 d the nested Dirichlet process on simulated datasets of different sizes\, 
 where groups differ in shape or in expectation\, and finally we will compa
 re the models on real world injury datasets.\n	 \n
DTSTART:20161104T200000Z
DTEND:20161104T203000Z
LOCATION:room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Alexandre Piché\, 9IÖÆ×÷³§Ãâ·Ñ
URL:/mathstat/channels/event/alexandre-piche-mcgill-un
 iversity-263868
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