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Joint models for longitudinal and survival data

Project

Project Details

Program
Statistics
Field of Study
​Statistics
Division
Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link
https://www.kaust.edu.sa/en/study/faculty/haavard-rue

Project Description

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.​​

About the Researcher

Haavard Rue
Professor, Statistics
Computer, Electrical and Mathematical Science and Engineering Division

Affiliations

Education Profile

  • PhD Norwegian Institute of Technology, 1993
  • MEng Norwegian Institute of Technology, 1988

Research Interests

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.

Selected Publications

  • H. Rue, S. Martino, and N. Chopin. a€œApproximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations (with dis-cussion)a€. In: Journal of the Royal Statistical Society, Series B 71.2 (2009), pp. 319a-392.
  • F. Lindgren, H. Rue, and J. LindstrA¶m. a€œAn explicit link between Gaussian fields and Gaussian Markov random fields: The SPDE approach (with discussion)a€. In: Journal of the Royal Statistical Society, Series B 73.4 (2011), pp. 423a-498.
  • D. Simpson, J. Illian, F. Lindgren, S. SA¸rbye, and H. Rue. a€œGoing off grid: Com-putational efficient inference for log-Gaussian Cox processesa€. In: Biometrika 103.1 (2016). (doi: 10.1093/biomet/asv064), pp. 1a-22.
  • D. P. Simpson, H. Rue, T. G. Martins, A. Riebler, and S. H. SA¸rbye. Penalising model component complexity: A principled, practical approach to constructing priors. arXiv:1403.4630 (revised in 2015). NTNU, Trondheim, Norway, 2014.
  • H. Rue and L. Held. Gaussian Markov Random Fields: Theory and Applications. Vol. 104. Monographs on Statistics and Applied Probability. London: Chapman & Hall, 2005.

Desired Project Deliverables

​Vignettes for the implementation of joint models within the R-INLA framework​
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