1 citations · 1 across the 2 of their papers we have counts for
4 papers
Fitting latent non-Gaussian models using variational Bayes and Laplace approximations
Rafael Cabral, David Bolin, Håvard Rue
Latent Gaussian models (LGMs) are perhaps the most commonly used class of models in statistical applications. Nevertheless, in areas ranging from longitudinal studies in biostatist…
Joint Quantile Disease Mapping with Application to Malaria and G6PD Deficiency
Hanan Alahmadi, Håvard Rue, Janet van Niekerk
Statistical analysis based on quantile regression methods is more comprehensive, flexible, and less sensitive to outliers when compared to mean regression methods. When the link be…
Discrete versus continuous domain models for disease mapping
Garyfallos Konstantinoudis, Dominic Schuhmacher, Håvard Rue +1
The main goal of disease mapping is to estimate disease risk and identify high-risk areas. Such analyses are hampered by the limited geographical resolution of the available data.…
Spatial modelling with R-INLA: A review
Haakon Bakka, Håvard Rue, Geir-Arne Fuglstad +5
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realist…