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UID:20251109T032914EST-5008o6k4Lo@132.216.98.100
DTSTAMP:20251109T082914Z
DESCRIPTION:Title: Boosting Learning of Censored Survival Data.\n\nAbstract
 : Survival data frequently arise from cancer research\, biomedical studies
 \, and clinical trials. Survival analysis has attracted extensive research
  interests in the past five decades. Numerous modeling strategies and infe
 rential procedures have been developed in the literature. In this talk\, I
  will start with a brief introductory overview of classical survival analy
 sis which centers around statistical inference\, and then discuss a boosti
 ng method which focuses on prediction. While boosting methods have been we
 ll known in the field of machine learning\, they have also been broadly di
 scussed in the statistical community for various settings\, especially for
  cases with complete data. This talk concerns survival data which typicall
 y involve censored responses. Three adjusted loss functions are proposed t
 o address the effects due to right-censored responses where no specific mo
 del is imposed\, and an unbiased boosting estimation method is developed. 
 Theoretical results\, including consistency and convergence\, are establis
 hed. Numerical studies demonstrate the promising finite sample performance
  of the proposed method.\n\nhttps://uqam.zoom.us/j/83461849475\n
DTSTART:20211111T203000Z
DTEND:20211111T213000Z
SUMMARY:Grace Yi (University of Western Ontario)
URL:/mathstat/channels/event/grace-yi-university-weste
 rn-ontario-334648
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