Joint models for longitudinal and survival dataApply
Joint models are essential is most biomedical applications due to their ability to model two data types simultaneously. In this project, the aim istodevelop vignettes for the implementation of joint models within theR-INLAframework. The prospective candidate would gain coding skills in R and potentially develop new statistical methodologies to handle real-world phenomena within the context of joint models.
Program - Applied Mathematics and Computer Science
Division - Computer, Electrical and Mathematical Sciences and Engineering
Field of Study - Statistics
Professor Rue's research interests lie in computational Bayesian statistics and Bayesian methodology such as priors, sensitivity and robustness. His main body of research is built around the R-INLA project (www.r-inla.org), which aims to provide a practical tool for approximate Bayesian analysis of latent Gaussian models, often at extreme data scales. This project also includes efforts to use stochastic partial differential equations to represent Gaussian fields, for the use in spatial statistics.
Desired Project Deliverables
Vignettes for the implementation of joint models within the R-INLA framework