80 citations · 127 across the 12 of their papers we have counts for
5 papers · 1 filter
Competing risks joint models using R-INLA
Janet van Niekerk, Haakon Bakka, Haavard Rue
The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models have largely been neglected, especially from…
New frontiers in Bayesian modeling using the INLA package in R
Janet van Niekerk, Haakon Bakka, Haavard Rue +1
The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models. It…
Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models
Birgir Hrafnkelsson, Stefan Siegert, Raphaël Huser +2
With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayes…
Approximate Bayesian inference for analysis of spatio-temporal flood frequency data
Árni V. Johannesson, Stefan Siegert, Raphaël Huser +2
Extreme floods cause casualties, and widespread damage to property and vital civil infrastructure. We here propose a Bayesian approach for predicting extreme floods using the gener…
Joint models as latent Gaussian models - not reinventing the wheel
Janet Van Niekerk, Haakon Bakka, Haavard Rue
Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and…