13 citations · 33 across the 25 of their papers we have counts for
28 papers
Flexible covariance structures on metric graphs
Karina Lilleborge, Sara Martino, Geir-Arne Fuglstad
Whittle-Matérn (WM) Gaussian random fields (GRFs) are defined as solutions of stochastic partial differential equations (SPDEs) and provide a natural analog of Matérn GRFs on non-E…
Sampling distributions for complex design variance estimators in a Fay-Herriot model
Alana McGovern, Geir-Arne Fuglstad, Jon Wakefield
Fay-Herriot (FH) models with variance smoothing typically use chi-squared sampling distributions for the design variance estimators. This choice is only valid under strong assumpti…
ARMA approximation of a Non-separable Spatio-Temporal Model with Fractional Smoothnesses in Space and Time
S. Knutsen Furset, Geir-Arne Fuglstad, Espen R. Jakobsen
The Matérn covariance model is ubiquitous in spatial modelling, but there is no default choice for spatio-temporal modelling. In this paper, we consider the recently proposed ``dif…
Non-stationary Spatial Modeling Using Fractional SPDEs
Elling Svee, Geir-Arne Fuglstad
We construct a Gaussian random field (GRF) that combines fractional smoothness with spatially varying anisotropy. The GRF is defined through a stochastic partial differential equat…
Surface finite element approximation of parabolic SPDEs with Whittle--Matérn noise
Øyvind Stormark Auestad, Geir-Arne Fuglstad, Annika Lang
We propose and analyse a new type of fully discrete surface finite element approximation of a class of linear parabolic stochastic evolution equations with additive noise. Our disc…
Joint Modelling of Line and Point Data on Metric Graphs
Karina Lilleborge, Sara Martino, Geir-Arne Fuglstad +2
Metric graphs are useful tools for describing spatial domains like road and river networks, where spatial dependence act along the network. We take advantage of recent developments…
Finite element approximation of parabolic SPDEs with Whittle--Matérn noise
Øyvind Stormark Auestad, Geir-Arne Fuglstad, Espen Robstad Jakobsen +1
We propose and analyse a new type of fully discrete finite element approximation of a class of linear stochastic parabolic evolution equations with additive noise. Our discretizati…
Non-stationary Spatio-Temporal Modeling Using the Stochastic Advection-Diffusion Equation
Martin Outzen Berild, Geir-Arne Fuglstad
We construct flexible spatio-temporal models through stochastic partial differential equations (SPDEs) where both diffusion and advection can be spatially varying. Computations are…
Spatial fusion of DHS and MICS surveys with jittered and geomasked locations
John Paige, Geir-Arne Fuglstad, Andrea Riebler
In low- and middle-income countries, survey data is a key data source for public health monitoring. Not accounting for the anonymization applied to the GPS locations in these surve…
GeoAdjust: Adjusting for Positional Uncertainty in Geostatistial Analysis of DHS Data
Umut Altay, John Paige, Andrea Riebler +1
The R-package GeoAdjust https://github.com/umut-altay/GeoAdjust-package implements fast empirical Bayesian geostatistical inference for household survey data from the Demographic a…
High Resolution Global Precipitation Downscaling with Latent Gaussian Models and Nonstationary SPDE Structure
Jiachen Zhang, Matthew Bonas, Diogo Bolster +2
Obtaining high-resolution maps of precipitation data can provide key insights to stakeholders to assess a sustainable access to water resources at urban scale. Mapping a nonstation…
Spatially Varying Anisotropy for Gaussian Random Fields in Three-Dimensional Space
Martin Outzen Berild, Geir-Arne Fuglstad
Isotropic covariance structures can be unreasonable for phenomena in three-dimensional spaces such as the ocean. In the ocean, the variability of the response may vary with depth,…
Impact of Jittering on Raster- and Distance-based Geostatistical Analyses of DHS Data
Umut Altay, John Paige, Andrea Riebler +1
Fine-scale covariate rasters are routinely used in geostatistical models for mapping demographic and health indicators based on household surveys from the Demographic and Health Su…
Spatial Aggregation with Respect to a Population Distribution
John Paige, Geir-Arne Fuglstad, Andrea Riebler +1
Spatial aggregation with respect to a population distribution involves estimating aggregate quantities for a population based on an observation of individuals in a subpopulation. I…
Fast geostatistical inference under positional uncertainty: Analysing DHS household survey data
Umut Altay, John Paige, Andrea Riebler +1
Household survey data from the Demographic and Health Surveys (DHS) Program is published with GPS coordinates. However, almost all geostatistical analyses of such data ignore that…
The Two Cultures of Prevalence Mapping: Small Area Estimation and Model-Based Geostatistics
Jon Wakefield, Peter A. Gao, Geir-Arne Fuglstad +1
In low- and middle-income countries (LMICs), accurate estimates of subnational health and demographic indicators are critical for guiding policy and identifying disparities. Many i…
makemyprior: Intuitive Construction of Joint Priors for Variance Parameters in R
Ingeborg Gullikstad Hem, Geir-Arne Fuglstad, Andrea Riebler
Priors allow us to robustify inference and to incorporate expert knowledge in Bayesian hierarchical models. This is particularly important when there are random effects that are ha…
Space-Time Smoothing of Survey Outcomes using the R Package SUMMER
Zehang Richard Li, Bryan D Martin, Tracy Qi Dong +5
The increasing availability of complex survey data, and the continued need for estimates of demographic and health indicators at a fine spatial and temporal scale, which leads to i…
Bayesian Multiresolution Modeling Of Georeferenced Data
John Paige, Geir-Arne Fuglstad, Andrea Riebler +1
Current implementations of multiresolution methods are limited in terms of possible types of responses and approaches to inference. We provide a multiresolution approach for spatia…
Design- and Model-Based Approaches to Small-Area Estimation in a Low and Middle Income Country Context: Comparisons and Recommendations
John Paige, Geir-Arne Fuglstad, Andrea Riebler +1
The need for rigorous and timely health and demographic summaries has provided the impetus for an explosion in geographic studies, with a common approach being the production of pi…
Intuitive joint priors for variance parameters
Geir-Arne Fuglstad, Ingeborg Gullikstad Hem, Alexander Knight +2
Variance parameters in additive models are typically assigned independent priors that do not account for model structure. We present a new framework for prior selection based on a…
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…
Estimating Under Five Mortality in Space and Time in a Developing World Context
Jon Wakefield, Geir-Arne Fuglstad, Andrea Riebler +3
Accurate estimates of the under-5 mortality rate (U5MR) in a developing world context are a key barometer of the health of a nation. This paper describes new models to analyze surv…
Modeling water supply networks and gastrointestinal disorder symptoms with CAR models
Håvard Wahl Kongsgård, Geir-Arne Fuglstad, Håvard Rue +2
Background: The direct modeling of water networks is not a common practice in modern epidemiology. While space often serves as a proxy, it can be problematic. There are multiple wa…