Finn Lindgren
Finn Lindgren
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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
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
Bayesian spatial modelling with R-INLA
F Lindgren, H Rue
Journal of statistical software 63 (19), 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
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, 109-131, 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
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
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
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
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
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, 523-550, 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
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
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
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
On the second‐order random walk model for irregular locations
F Lindgren, H Rue
Scandinavian journal of statistics 35 (4), 691-700, 2008
Think continuous: Markovian Gaussian models in spatial statistics
D Simpson, F Lindgren, H Rue
Spatial Statistics 1, 16-29, 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
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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