Items by Lindgren, Finn
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Number of items: 20.
Nychka, D., Bandyopadhyay, S., Hammerling, D., Lindgren, F. and Sain, S., 2015. A Multi-resolution Gaussian process model for the analysis of large spatial data sets. Journal of Computational and Graphical Statistics, 24 (2), pp. 579-599.
Zammit-Mangion, A., Rougier, J., Schön, N., Lindgren, F. and Bamber, J., 2015. Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise. Environmetrics, 26 (3), pp. 159-177.
Lindgren, F., 2015. Comments on:Comparing and selecting spatial predictors using local criteria. Test, 24 (1), pp. 35-44. Item availability may be restricted.
Lindgren, F. and Rue, H., 2015. Bayesian Spatial Modelling with R-INLA. Journal of Statistical Software, 63 (19).
Bolin, D. and Lindgren, F., 2015. Excursion and contour uncertainty regions for latent Gaussian models. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 77 (1), pp. 85-106.
Yue, Y. R., Simpson, D., Lindgren, F. K. and Rue, H., 2014. Bayesian adaptive smoothing splines using stochastic differential equations. Bayesian Analysis, 9 (2), p. 397.
Ingebrigtsen, R., Lindgren, F. K. and Steinsland, I., 2014. Spatial models with explanatory variables in the dependence structure. Spatial Statistics, 8, p. 20.
Martins, T. G., Simpson, D., Lindgren, F. K. and Rue, H., 2013. Bayesian computing with INLA : New features. Computational Statistics & Data Analysis, 67, pp. 68-83.
Bolin, D. and Lindgren, F., 2013. A comparison between Markov approximations and other methods for large spatial data sets. Computational Statistics & Data Analysis, 61, pp. 7-21.
Simpson, D., Lindgren, F. and Rue, H., 2012. Think continuous:Markovian Gaussian models in spatial statistics. Spatial Statistics, 1, pp. 16-29.
Cameletti, M., Lindgren, F., Simpson, D. and Rue, H., 2012. Spatio-temporal modeling of particulate matter concentration through the SPDE approach. AStA Advances in Statistical Analysis, 97 (2), pp. 109-131.
Simpson, D., Lindgren, F. and Rue, H., 2012. In order to make spatial statistics computationally feasible, we need to forget about the covariance function. Environmetrics, 23 (1), pp. 65-74.
Lindgren, F., Rue, H. and Lindström, J., 2011. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 73 (4), pp. 423-498.
Lindgren, G. and Lindgren, F., 2011. Stochastic asymmetry properties of 3D gauss-lagrange ocean waves with directional spreading. Stochastic Models, 27 (3), pp. 490-520.
Lindgren, F., Martins, T., Rue, H. and Simpson, D., 2011. Discussion on "Spatial prediction in the presence of positional error". Environmetrics, 22 (2), p. 127.
Bolin, D. and Lindgren, F., 2011. Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping. Annals of Applied Statistics, 5 (1), pp. 523-550.
Lindgren, G., Bolin, D. and Lindgren, F., 2010. Non-traditional stochastic models for ocean waves. European Physical Journal - Special Topics, 185 (1), pp. 209-224.
Bolin, D., Lindström, J., Lindgren, F. and Eklundh, L., 2009. Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields. Computational Statistics & Data Analysis, 53 (8), pp. 2885-2896.
Lindgren, F. and Rue, H., 2008. On the second-order random walk model for irregular locations. Scandinavian Journal of Statistics, 35 (4), pp. 691-700.