Event
Alexandre Pich茅, 平特五不中
Friday, November 4, 2016 16:00to16:30
Burnside Hall
room 1205, 805 rue Sherbrooke Ouest, Montreal, QC, H3A 0B9, CA
Bayesian nonparametric modeling of heterogeneous groups of censored data.
Analysis of survival data arising from different groups, whereby the data in each group is scarce, but abundant overall, is a common issue in applied statistics. Bayesian nonparametrics are tools of choice to handle such datasets given their ability to share information across groups. In this presentation, we will compare three popular Bayesian nonparametric methods on the modeling of survival functions coming from related heterogeneous groups. Specifically, we will first compare the modeling accuracy of the Dirichlet process, the hierarchical Dirichlet process, and the nested Dirichlet process on simulated datasets of different sizes, where groups differ in shape or in expectation, and finally we will compare the models on real world injury datasets.
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