Older publications and technical reports Publications OverviewPeer-reviewed Publications before 2019P. Sidén, F. Lindgren, D. Bolin, M. Villani (2018), Efficient Covariance Approximations for Large Sparse Precision Matrices, Journal of Computational and Graphical Statistics, 27:4, 898-909J. Wallin and D. Bolin (2018), Efficient adaptive MCMC through precision estimation, Journal of Computational and Graphical Statistics, 27:4, 887-897D. Bolin, K. Kirchner, M. Kovács (2018), Weak convergence of Galerkin approximations for fractional elliptic stochastic PDEs with spatial white noise, BIT Numerical Mathematics, 58:4, 881-906.A. Hildeman, D. Bolin, J. Wallin, J. Illian (2018), Level set Cox processes, Spatial Statistics, 28, 169-193.H. Bakka, H. Rue, G.-A. Fuglstad, A. Riebler, D. Bolin, E. Krainski, D. Simpson, and F. Lindgren (2018), Spatial modelling with R-INLA: A review, WIREs Computational Statistics, 10:e1443.D. Bolin and F. Lindgren (2018), Calculating probabilistic excursion sets and related quantities using excursions, Journal of Statistical Software, 86(5), 1-20.K. Kuljus, F.L. Bayisa, D. Bolin, J. Lember, J. Yu (2018), Comparison of hidden Markov chain models and hidden Markov random field models in estimation of computed tomography images, Communications in Statistics: Case Studies, Data Analysis and Applications, 4, 46-55.S. Barman and D. Bolin (2018), A three-dimensional statistical model for imaged microstructures of porous polymer films, Journal of Microscopy, 269: 247–258.P. Sidén, A. Eklund, D. Bolin, M. Villani (2017), Fast Bayesian whole-brain fMRI analysis with spatial 3D priors, NeuroImage, 146, 211–225.D. Bolin and F. Lindgren (2017), Quantifying the uncertainty of contour maps, Journal of Computational and Graphical Statistics, 26:3, 513-524.C Gustafson, D Bolin, F Tufvesson (2016), Modeling the polarimetric mm-wave propagation channel using censored measurements, 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, 1-6.D. Bolin, A. Frigessi, P. Guttorp, O. Haug, E. Orskaug, I. Scheel, and J. Wallin (2016), Calibrating regionally downscaled precipitation over Norway through quantile-based approaches, Advances in Statistical Climatology, Meteorology and Oceanography, 2, 39-47.D. Bolin and J. Wallin (2016), Spatially adaptive covariance tapering, Spatial Statistics, 18, 163-178.C. Gustafson, T. Abbas, D. Bolin, F. Tufvesson (2015), Statistical Modeling and Estimation of Censored Pathloss Data, IEEE Wireless Communications Letters, 4,5, 569-572.J. Wallin and D. Bolin (2015), Geostatistical Modelling Using Non-Gaussian Matérn Fields, Scandinavian Journal of Statistics, 42,3, 872-890.D. Bolin, P. Guttorp, A. Januzzi, D. Jones, M. Novak, H. Podschwit, L. Richardson, A. Särkkä, C. Sowder, and A. Zimmerman (2015), Statistical prediction of global sea level from global temperature, Statistica Sinica, 25, 351-367.D. Bolin and F. Lindgren (2015), Excursion and contour uncertainty regions for latent Gaussian models, Journal of the Royal Statistical Society, Series B Methodology, 77, 1, 85-106.P. Guttorp, A. Januzzi, M. Novak, H. Podschwit, L. Richardson, C.D. Sowder, A. Zimmerman, D. Bolin, and A. Särkkä (2014), Assessing the uncertainty in projecting local mean sea level from global temperature, Journal of Applied Meteorology and Climatology, 53, 2163-2170.C. Gustafson, D. Bolin, and F. Tufvesson (2014), Modeling the cluster decay in mm-Wave channels, The 8th European Conference on Antennas and Propagation (EuCAP 2014), The Hague, 804-808.D. Bolin (2014), Spatial Matérn fields driven by non-Gaussian noise, Scandinavian Journal of Statistics, 41:3, 557-579.D. Bolin and F. Lindgren (2013), A comparison between Markov approximations and other methods for large spatial data sets, Computational Statistics and Data Analysis, 61, 7-21.D. Bolin and F. Lindgren (2011), Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping, Annals of Applied Statistics, 5, 1, 523-550.G. Lindgren, D. Bolin, and F. Lindgren (2010), Non-traditional stochastic models for ocean waves, Lagrange models and nested SPDE models, European Physical Journal, Special Topics 185, 209-224.D. Bolin, J. Lindström, L. Eklundh, and F. Lindgren (2009), Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields, Computational Statistics and Data Analysis 53, 2885-2896.Technical reports and other publicationsD. Spencer, D. Bolin, M. B. Nebel, A. Mejia (2022) Fast Bayesian estimation of brain activation with cortical surface and subcortical fMRI data using EMD. Bolin (2022) Spatial field, in Wiley StatsRef: Statistics Reference OnlineA. Hildeman, D. Bolin, J. Wallin, A. Johansson, T. Nyholm, T. Asklund, J. Yu, Whole-brain substitute CT generation using Markov random field mixture models (2016)C. Gustafson, T. Abbas, D. Bolin and F. Tufvesson, Tobit Maximum-likelihood estimation of Censored Pathloss Data. (2015)B.N. Hellquist, D. Bolin, J. Yu, H. Jonsson, Poisson based model for adjusting for non-compliance and contamination in cohort studies, in thesis "Breast cancer screening with mammography of women 40-49 years in Sweden". (2014)Y. Yue, M. Lindquist, D. Bolin, F. Lindgren, D. Simpson, and H. Rue, A Bayesian General Linear Modeling Approach to fMRI Data Analysis (2013)
