| The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015 S Bhatt, DJ Weiss, E Cameron, D Bisanzio, B Mappin, U Dalrymple, ... Nature 526 (7572), 207-211, 2015 | 3066 | 2015 |
| An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach F Lindgren, H Rue, J Lindström Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2011 | 2949 | 2011 |
| Bayesian spatial modelling with R-INLA F Lindgren, H Rue Journal of statistical software 63 (19), 2015 | 1259 | 2015 |
| Bayesian computing with INLA: a review H Rue, A Riebler, SH Sørbye, JB Illian, DP Simpson, FK Lindgren Annual Review of Statistics and Its Application 4 (1), 395-421, 2017 | 748 | 2017 |
| Bayesian computing with INLA: new features TG Martins, D Simpson, F Lindgren, H Rue Computational Statistics & Data Analysis 67, 68-83, 2013 | 670 | 2013 |
| Spatio-temporal modeling of particulate matter concentration through the SPDE approach M Cameletti, F Lindgren, D Simpson, H Rue AStA Advances in Statistical Analysis 97, 109-131, 2013 | 431 | 2013 |
| A case study competition among methods for analyzing large spatial data MJ Heaton, A Datta, AO Finley, R Furrer, J Guinness, R Guhaniyogi, ... Journal of Agricultural, Biological and Environmental Statistics 24, 398-425, 2019 | 428 | 2019 |
| Constructing priors that penalize the complexity of Gaussian random fields GA Fuglstad, D Simpson, F Lindgren, H Rue Journal of the American Statistical Association 114 (525), 445-452, 2019 | 410 | 2019 |
| A multiresolution Gaussian process model for the analysis of large spatial datasets D Nychka, S Bandyopadhyay, D Hammerling, F Lindgren, S Sain Journal of Computational and Graphical Statistics 24 (2), 579-599, 2015 | 385 | 2015 |
| Spatial modeling with R‐INLA: A review H Bakka, H Rue, GA Fuglstad, A Riebler, D Bolin, J Illian, E Krainski, ... Wiley Interdisciplinary Reviews: Computational Statistics 10 (6), e1443, 2018 | 374 | 2018 |
| Advanced spatial modeling with stochastic partial differential equations using R and INLA E Krainski, V Gómez-Rubio, H Bakka, A Lenzi, D Castro-Camilo, ... Chapman and Hall/CRC, 2018 | 326 | 2018 |
| Going off grid: Computationally efficient inference for log-Gaussian Cox processes D Simpson, JB Illian, F Lindgren, SH Sørbye, H Rue Biometrika 103 (1), 49-70, 2016 | 273 | 2016 |
| Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping D Bolin, F Lindgren The Annals of Applied Statistics, 523-550, 2011 | 155 | 2011 |
| Excursion and contour uncertainty regions for latent Gaussian models D Bolin, F Lindgren Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015 | 154 | 2015 |
| inlabru: an R package for Bayesian spatial modelling from ecological survey data FE Bachl, F Lindgren, DL Borchers, JB Illian Methods in Ecology and Evolution 10 (6), 760-766, 2019 | 147 | 2019 |
| Exploring a new class of non-stationary spatial Gaussian random fields with varying local anisotropy GA Fuglstad, F Lindgren, D Simpson, H Rue Statistica Sinica, 115-133, 2015 | 139 | 2015 |
| In order to make spatial statistics computationally feasible, we need to forget about the covariance function D Simpson, F Lindgren, H Rue Environmetrics 23 (1), 65-74, 2012 | 129 | 2012 |
| On the second‐order random walk model for irregular locations F Lindgren, H Rue Scandinavian journal of statistics 35 (4), 691-700, 2008 | 128 | 2008 |
| Think continuous: Markovian Gaussian models in spatial statistics D Simpson, F Lindgren, H Rue Spatial Statistics 1, 16-29, 2012 | 126 | 2012 |
| Does non-stationary spatial data always require non-stationary random fields? GA Fuglstad, D Simpson, F Lindgren, H Rue Spatial Statistics 14, 505-531, 2015 | 125 | 2015 |
Håvard RueKing Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
