Background and activities
My main interests lie in spatio-temporal statistics and computational statistics. In particular, the use of stochastic partial differential equation (SPDE) models for fast computations with spatio-temporal processes. I am interested in applications in a wide range of scientific fields from epidemiology and demography to physical sciences.
From 2020 we are starting the project MASCOT (Maritime Autonomous Sampling and Control). This is an inter-disciplinary project that aims to develop and design observational strategies in spatio-temporal domains, enabling autonomous platforms to decide on a strategy of where and when to make measurements to increase our knowledge of dynamic environments like the upper water-column.
I'm involved in the development of the R-package SUMMER (Spatio-Temporal Under-Five Mortality Methods for Estimation), where the target is to take advantage of complex, but easy-to-use and computationally efficient methods for subnational mapping of under-five mortality in low- and medium-income countries. This work is lead by Jon Wakefield at the University of Washington, Seattle, USA. The methods and software were used to produce official subnational under-five mortality estimates for 1990-2019 in close coordination with the United Nations Inter-agency Group for Mortality Estimation (UN IGME).
I'm also interested in methodological development in Bayesian statistics, and I'm taking part in the project "Penalized Complexity-priors: A new tool to define default priors and robustify Bayesian models" (NFR), which is lead by Andrea Riebler at IMF, NTNU. This project aims to create a framework for specifying intuitive and robust joint priors for several variance parameters in a latent Gaussian model. The methods are available in the R-package makemyprior.
See here for more details.
Scientific, academic and artistic work
Displaying a selection of activities. See all publications in the database
- (2021) Robust Modelling of Additive and Non-additive Variation with Intuitive Inclusion of Expert Knowledge. Genetics. vol. 217 (3).
- (2020) Compression of Climate Simulations with a Nonstationary Global Spatio-Temporal SPDE Model. Annals of Applied Statistics. vol. 14 (2).
- (2020) Intuitive Joint Priors for Variance Parameters. Bayesian Analysis. vol. 15 (4).
- (2020) Predominant regional biophysical cooling from recent land cover changes in Europe. Nature Communications. vol. 11.
- (2020) Design- and Model-Based Approaches to Small-Area Estimation in a Low and Middle Income Country Context: Comparisons and Recommendations. Journal of Survey Statistics and Methodology.
- (2019) Constructing Priors that Penalize the Complexity of Gaussian Random Fields. Journal of the American Statistical Association. vol. 114 (525).
- (2019) Estimating under-five mortality in space and time in a developing world context. Statistical Methods in Medical Research. vol. 28 (9).
- (2018) Spatial modelling with R-INLA: A review. Wiley Interdisciplinary Reviews: Computational Statistics. vol. 10:e1443 (6).
- (2018) Environmental mapping using Bayesian spatial modelling (INLA/SPDE): A reply to Huang et al. (2017). Science of the Total Environment. vol. 624.
- (2017) Predicting soil properties in the Canadian boreal forest with limited data: Comparison of spatial and non-spatial statistical approaches. Geoderma. vol. 306.
- (2016) Assessing comorbidity and correlates of wasting and stunting among children in Somalia using cross-sectional household surveys: 2007 to 2010. BMJ Open. vol. 6:e009854 (3).
- (2016) Landscape relatedness: detecting contemporary fine-scale spatial structure in wild populations. Landscape Ecology. vol. 32 (1).