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Department seminar by Rodney Sparapani, Medical College of Wisconsin   Export this event to calendar

Thursday, September 12, 2019 — 4:00 PM EDT

Nonparametric failure time with Bayesian Additive Regression Trees


Bayesian Additive Regression Trees (BART) is a nonparametric machine learning method for continuous, dichotomous, categorical and time-to-event outcomes.  However, survival analysis with BART currently presents some challenges.  Two current approaches each have their pros and cons.  Our discrete time approach is free of precarious  restrictive assumptions such as proportional hazards and Accelerated Failure Time (AFT), but it becomes increasingly computationally demanding as the sample size increases.  Alternatively, a Dirichlet Process Mixture approach is computationally friendly, but it suffers from the AFT assumption.  Therefore, we propose to further nonparametrically enhance this latter approach via heteroskedastic BART which will remove the restrictive AFT assumption while maintaining its desirable computational properties.​​​​​​​

Location 
M3 - Mathematics 3
Room: 3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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