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UID:20260625T131006EDT-9777HBVGVE@132.216.98.100
DTSTAMP:20260625T171006Z
DESCRIPTION:Title: MAPLE\; Semiparametric Estimation and Variable Selection
  for Length-biased Data with Heavy Censoring.\n\n\n	Abstract:\n\n\nIn this 
 talk\, we discuss two problems of semiparametric estimation and variable s
 election for length-biased data with heavy censoring. The common feature o
 f the proposed estimation procedures in the literature is that they only p
 ut probability mass on failure times. Under length-biased sampling\, howev
 er\, censoring is informative and failing to incorporate censored observat
 ions into estimation can lead to a substantial loss of efficiency. We prop
 ose two estimation procedures by computing the likelihood contribution of 
 both uncensored and censored observations. For variable selection problem\
 , we introduce a unified penalized estimating function and use an optimiza
 tion algorithm to solve it. We discuss the asymptotic properties of the re
 sulting penalized estimators. The work is motivated by the International s
 troke Trial dataset collected in Argentina in which the survival times of 
 about 88% of the 545 cases are censored.\n\n\n	Speaker\n\n\nOmid Aghababaei
  is a Postdoctoral Fellow at The Hospital for Sick Children\, Peter Gilgan
  Centre for Research & Learning\, Child Health Evaluative Sciences. His su
 pervisors are Professor Masoud Asgharian and Prof. Abbas Khalili\n
DTSTART:20190906T193000Z
DTEND:20190906T203000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Jazi Omidali Aghababaei
URL:/mathstat/channels/event/jazi-omidali-aghababaei-3
 00283
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