My research interests are in stochastic modelling and computational methods for spatial process data.
Enviromental applications, such as climate science, pollution monitoring, and ecology, involve data measured with random measurement variability at irregular points in space and time. This necessitates the use of statistical models and methods to quantify the uncertainty about the underlying spatial and spatio-temporal processes, for example historical global temperatures. In my research, I am interested both in methods for modelling these processes as structured random quantities, and in computationally efficient methods for estimation of model parameters, spatial reconstruction, and temporal prediction based on incomplete but large data sets. A important aspect is development of general software, allowing applied scientists to apply these methods to their own problems. This requires combining methods and techniques from several branches of the mathematical sciences, in particular probability theory for stochastic processes, Bayesian statistics, and numerical analysis.
Simpson, D., Illian, J. B., Lindgren, F., Sorbye, S. H. and Rue, H., 2016. Going off grid:computationally efficient inference for log-Gaussian Cox processes. Biometrika, 103 (1), pp. 49-70.
Fuglstad, G.-A., Simpson, D., Lindgren, F. and Rue, H., 2015. Does non-stationary spatial data always require non-stationary random fields? Spatial Statistics, 14 (Part C), pp. 505-531.
Ingebrigtsen, R., Lindgren, F., Steinsland, I. and Martino, S., 2015. Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field. Spatial Statistics, 14 (Part C), pp. 338-364.
Bhatt, S., Weiss, D. J., Cameron, E., Bisanzio, D., Mappin, B., Dalrymple, U., Battle, K. E., Moyes, C. L., Henry, A., Eckhoff, P. A., Wenger, E. A., Briët, O., Penny, M. A., Smith, T. A., Bennett, A., Yukich, J., Eisele, T. P., Griffin, J. T., Fergus, C. A., Lynch, M., Lindgren, F., Cohen, J. M., Murray, C. L. J., Smith, D. L., Hay, S. I., Cibulskis, R. E. and Gething, P. W., 2015. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature, 526 (7572), pp. 207-211.
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.
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.
Fuglstad, G.-A., Lindgren, F., Simpson, D. and Rue, H., 2015. Exploring a New Class of Non-stationary Spatial Gaussian Random Fields with Varying Local Anisotropy. Statistica Sinica, 25 (1), pp. 115-133.
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.
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.
Gilleland, E., Lindström, J. and Lindgren, F., 2010. Analyzing the Image Warp Forecast Verification Method on Precipitation Fields from the ICP. Weather and Forecasting, 25 (4), pp. 1249-1262.
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.