Speaker: Dr. Grace Yi
Title: Enhancing Survival Data Analysis: Graphical Proportional Hazards Measurement Error Models
Abstract: Abstract In survival data analysis, the Cox proportional hazards (PH) model is perhaps the most widely used model to feature the dependence of survival times on covariates. While many inference methods have been developed under such a model or its variants, those models are not adequate for handling data with complex structured covariates. High-dimensional survival data often entail several features: (1) many covariates are inactive in explaining the survival information, (2) active covariates are associated in a network structure, and (3) some covariates are error-contaminated. To handle such survival data, we propose graphical PH measurement error models and develop inferential procedures for the parameters of interest. Our proposed models significantly enlarge the scope of the usual Cox PH model and have great flexibility in characterizing survival data. Theoretical results are established to justify the proposed methods. Numerical studies are conducted to assess the performance of the proposed methods.
Day & Time: Thursday, November 30, 2023 at 3:00pm
Location: Lambton 9-118
Counts toward seminar attendance for MSc and PhD students in Math & Stats.