Background and activities
I am a professor in statistics at Department of Mathematical Sciences. My research is in the area of computational (Bayesian) statistics and spatial statistics.
Håvard Rue is professor in statistics at the Department of Mathematical Sciences, Norwegian University of Science and Technology. His research interest includes Bayesian computing and spatial statistics, which is summarised through R-INLA package, see www.r-inla.org. He has been an associate editor for JRSS series-B, Scandinavian Journal of Statistics, Statistic Surveys, Annals of Statistics and Environmetrics. His main research interest has been Gaussian Markov random fields (GMRF) models, and with Leonhard Held he has written a monograph on the subject published by Chapman & Hall. GMRFs is also a main ingredient doing (fast and accurate) approximate Bayesian analysis for latent Gaussian models using integrated nested Laplace approximations (INLA), which is published as a discussion paper for JRSS series B 2009 co-authored with S.Martino and N.Chopin. Recent results also put GMRFs into geostatistics using stochastic partial differential equations as the bridge, which provides an explicit link between certain Gaussian fields and GMRFs in triangulated lattices (published as a discussion paper for JRSS series B in 2011, with F.Lindgren and J.Lindstrøm).
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
- (2017) Bayesian bivariate meta-analysis of diagnostic test studies with interpretable priors. Statistics in Medicine. vol. 36 (19).
- (2017) Bayesian Computing with INLA: A Review. Annual Review of Statistics and Its Application. vol. 4.
- (2016) Bayesian penalized spline models for the analysis of spatio-temporal count data. Statistics in Medicine. vol. 35 (11).
- (2016) An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research. vol. 25 (4).
- (2016) Going off grid: Computationally efficient inference for log-Gaussian Cox processes. Biometrika. vol. 103 (1).
- (2016) Penalized complexity priors for degrees of freedom in Bayesian P-splines. Statistical modelling. vol. 16 (6).
- (2016) A skew Gaussian decomposable graphical model. Journal of Multivariate Analysis. vol. 145.
- (2015) Spatial Data Analysis with R-INLA with Some Extensions. Journal of Statistical Software. vol. 63 (20).
- (2015) Improving the INLA approach for approximate Bayesian inference for latent Gaussian models. Electronic Journal of Statistics. vol. 9 (2).
- (2015) Does non-stationary spatial data always require non-stationary random fields?. Spatial Statistics. vol. 14.
- (2015) Exploring a new class of non-stationary spatial Gaussian random fields with varying local anisotropy. Statistica sinica. vol. 25.
- (2015) A new latent class to fit spatial econometrics models with Integrated Nested Laplace Approximations. Procedia Environmental Sciences. vol. 27.
- (2015) Bayesian spatial modelling with R-INLA. Journal of Statistical Software. vol. 63 (19).
- (2015) Bayesian analysis of measurement error models using integrated nested Laplace approximations. Journal of the Royal Statistical Society, Series C: Applied Statistics. vol. 64 (2).
- (2015) A Bayesian approach to estimate the biomass of anchovies in the coast of Peru. Biometrics. vol. 71 (1).
- (2015) Sensitivity analysis for Bayesian hierarchical models. Bayesian Analysis. vol. 10 (2).
- (2015) Beyond the valley of the covariance function. Statistical Science. vol. 30 (2).
- (2014) Approximate Bayesian inference for spatial econometrics models. Spatial Statistics. vol. 9.
- (2014) Geostatistical survival models for environmental risk assessment with large retrospective cohort. Journal of the Royal Statistical Society: Series A (Statistics in Society). vol. 177 (3).
- (2014) Extending Integrated Nested Laplace Approximation to a class of near-Gaussian latent models. Scandinavian Journal of Statistics. vol. 41 (4).