# Items by Lindgren, Finn

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**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.Simpson, D., Lindgren, F. and Rue, H., 2015. Beyond the valley of the covariance function.

*Statistical Science*, 30 (2), pp. 164-166.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.