Finn Lindgren
Finn Lindgren
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TitleCited byYear
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
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, 2015
Bayesian spatial modelling with R-INLA
F Lindgren, H Rue
Journal of Statistical Software 63 (19), 1-25, 2015
Bayesian computing with INLA: new features
TG Martins, D Simpson, F Lindgren, H Rue
Computational Statistics & Data Analysis 67, 68-83, 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 (2), 109-131, 2013
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
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, 395-421, 2017
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
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 5 (1), 523-550, 2011
Think continuous: Markovian Gaussian models in spatial statistics
D Simpson, F Lindgren, H Rue
Spatial Statistics 1, 16-29, 2012
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
INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion
H Rue, S Martino, F Lindgren, D Simpson, A Riebler, ET Krainski
R package version 0.0-1404466487, URL http://www. R-INLA. org, 2014
Excursion and contour uncertainty regions for latent Gaussian models
D Bolin, F Lindgren
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2015
On the second‐order random walk model for irregular locations
F Lindgren, H Rue
Scandinavian journal of statistics 35 (4), 691-700, 2008
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
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
Spatial models with explanatory variables in the dependence structure
R Ingebrigtsen, F Lindgren, I Steinsland
Spatial Statistics 8, 20-38, 2014
INLA: functions which allow to perform a full Bayesian analysis of structured additive models using Integrated Nested Laplace Approximation
H Rue, S Martino, F Lindgren, D Simpson, A Riebler, ET Krainski
R package version 0.0, 2009
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 (3), 398-425, 2019
A call for new approaches to quantifying biases in observations of sea surface temperature
EC Kent, JJ Kennedy, TM Smith, S Hirahara, B Huang, A Kaplan, ...
Bulletin of the American Meteorological Society 98 (8), 1601-1616, 2017
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