Seguir
Finn Lindgren
Finn Lindgren
Dirección de correo verificada de ed.ac.uk
Título
Citado por
Citado por
Año
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
28762015
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
27902011
Bayesian spatial modelling with R-INLA
F Lindgren, H Rue
Journal of statistical software 63 (19), 2015
11682015
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
6792017
Bayesian computing with INLA: new features
TG Martins, D Simpson, F Lindgren, H Rue
Computational Statistics & Data Analysis 67, 68-83, 2013
6402013
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
4102013
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
3902019
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
3832019
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
3682015
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
3402018
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
2972018
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
2582016
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
1562011
Excursion and contour uncertainty regions for latent Gaussian models
D Bolin, F Lindgren
Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015
1492015
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
1342019
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
1262015
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
1242012
On the second‐order random walk model for irregular locations
F Lindgren, H Rue
Scandinavian journal of statistics 35 (4), 691-700, 2008
1212008
Think continuous: Markovian Gaussian models in spatial statistics
D Simpson, F Lindgren, H Rue
Spatial Statistics 1, 16-29, 2012
1202012
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
1172015
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20