Preprints available online

• D. Bolin, J. Wallin (2024) Spatial confounding under infill asymptotics
• D. Bolin, A. Simas, Z. Xiong (2023) Wasserstein complexity penalization priors: a new class of penalizing complexity priors
• S. Chaudhuri, M.A. Barceló, P. Juan, D. Varga, D. Bolin, H. Rue, M. Saez (2023) Enhanced spatial modeling on linear networks using Gaussian Whittle-Matérn fields
• A. Mejia, D. Bolin, D. Spencer, A. Eloyan (2023) Accurate estimation of functional brain connectivity via Bayesian ICA with population-derived priors
• Z. Zhang, D. Bolin, S. Engelke, R. Huser (2023) Extremal Dependence of Moving Average Processes Driven by Exponential-Tailed Lévy Noise
• R. Cabral, D. Bolin, H. Rue (2023) Robustness, model checking and latent Gaussian models
• D. Bolin, A. Simas, J. Wallin (2023) Statistical inference for Gaussian Whittle-Matérn fields on metric graphs
• D. Bolin, A. Simas, J. Wallin (2023) Markov properties of Gaussian random fields on compact metric graphs
• D. Spencer, D. Bolin, M. B. Nebel, A. Mejia (2022) Fast Bayesian estimation of brain activation with cortical surface and subcortical fMRI data using EM
• A. Hazra, R. Huser and D. Bolin (2022) Realistic and Fast Modeling of Spatial Extremes over Large Geographical Domains

Peer-reviewed Publications

2024+:

57. H. Olafsdottir, H. Rootzén and D. Bolin (2024+) Locally tail-scale invariant scoring rules for evaluation of extreme value forecasts, International Journal of Forecasting, in press
58. R. Cabral, D. Bolin, H. Rue (2024+) Fitting latent non-Gaussian models using variational Bayes and Laplace approximations, Journal of the American Statistical Association, online
59. D. Bolin, M. Kovács, V. Kumar, A. Simas (2024+) Regularity and numerical approximation of fractional elliptic differential equations on compact metric graphs, Mathematics of Computation, online
60. F. Lindgren, H. Bakka, D. Bolin, E. Krainski, H. Rue (2024+) A diffusion-based spatio-temporal extension of Gaussian Matérn fields, Statistics and Operations Research Transactions (SORT), accepted
61. D. Bolin, A. Simas and Z. Xiong (2024) Covariance-based rational approximations of fractional SPDEs for computationally efficient Bayesian inference, Journal of Computational and Graphical Statistics, 33(1), 64-74
62. D. Bolin, A. Simas and J. Wallin (2024) Gaussian Whittle-Matérn fields on metric graphs, Bernoulli, 30(2), 1611–1639

2023:

51. R. Cabral, D. Bolin, H. Rue (2023) Controlling the flexibility of non-Gaussian processes through shrinkage priors, Bayesian Analysis, 18(4): 1223-1246
52. A. F. Mejia, D. Bolin, Y. R. Yue, J. Wang, B. S. Caffo and M. B. Nebel (2023) Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference, Journal of Computational and Graphical Statistics, 32, 413-433
53. M. Karlson, D. Bolin, H. R. Bazié, A. S. Ouedraogo, B. Soro, J. Sanou, J. Bayala and M. Ostwald (2023) Exploring the Landscape Scale Influences of Tree Cover on Crop Yield in an Agroforestry Parkland Using Satellite Remote Sensing and Spatial Statistics, Journal of Arid Environments, 218, 105051
54. D. Bolin and K. Kirchner (2023) Equivalence of measures and asymptotically optimal linear prediction for Gaussian random fields with fractional-order covariance operators, Bernoulli, 29:2, 1476-1504
55. D. Bolin and J. Wallin (2023), Local Scale Invariance and Robustness of Proper Scoring Rules, Statistical Science, 38:1, 140-159

2022:

46. F. Lindgren, D. Bolin, and H. Rue (2022) The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running, Spatial Statistics, 50, 100599
47. A. Hildeman, D. Bolin, I. Rychlik (2022), Joint spatial modeling of significant wave height and wave period using the SPDE approach, Probabilistic Engineering Mechanics, 68, 103203
48. K. Kirchner and D. Bolin (2022) Necessary and sufficient conditions for asymptotically optimal linear prediction of random fields on compact metric spaces, Annals of Statistics, 50(2): 1038-1065
49. D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups, NeuroImage, 249, 118908

2021:

42. P. Sidén, F. Lindgren, D. Bolin, A. Eklund, and M. Villani (2021), Spatial 3D Matérn priors for fast whole-brain fMRI analysis, Bayesian Analysis, 16(4), 1251-1278
43. D. Bolin and J. Wallin (2021) Efficient methods for Gaussian Markov random fields under sparse linear constraints, Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
44. D. Bolin, V. Verendel, M. Berghauser Pont, I. Stavroulaki, O. Ivarsson, and E. Håkansson (2021) Functional ANOVA modelling of pedestrian counts on streets in three European cities, Journal of the Royal Statistical Society, Series A , 184:4, 1176-1198
45. H. Olafsdottir, H. Rootzén and D. Bolin (2021), Extreme rainfall events in the Northeastern USA become more frequent with rising temperatures, but their intensity distribution remains stable, Journal of Climate, 34:22, 8863-8877
46. S. Barman, C. Fager, M. Röding, N. Lorén, C. von Corswant, E. Olsson, D. Bolin, H. Rootzén (2021) New Characterization Measures of Pore Shape and Connectivity Applied to Coatings used for Controlled Drug Release, Journal of Pharmaceutical Sciences, 110-7, 2753-2764
47. A. Hildeman, D. Bolin, I. Rychlik (2021), Deformed SPDE models with an application to spatial modeling of significant wave height, Spatial Statistics, 42:

    100449.

2020:

36. C. Fager, S. Barman, M. Röding, A. Olsson, N. Lorén, C. von Corswant, D. Bolin, H. Rootzén, E. Olsson (2020), 3D High Spatial Resolution Visualisation and Quantification of Interconnectivity in Polymer Films, International Journal of Pharmaceutics, 587, 119622.
37. Ö. Asar, D. Bolin, P. J. Diggle, J. Wallin (2020) Linear Mixed-Effects Models for Non-Gaussian continuous Repeated Measurement Data (with discussion), Journal of the Royal Statistical Society, Series C, 69:5, 1015-1065.
38. D. Bolin, K. Kirchner (2020), The rational SPDE approach for Gaussian random fields with general smoothness, Journal of Computational and Graphical Statistics, 29:2, 274-285.
39. C. Gustafson, K. Mahler, D. Bolin, F. Tufvesson (2020), The COST IRACON Geometry-based Stochastic Channel Model for Vehicle-to-Vehicle Communication in Intersections,  IEEE Transactions on Vehicular Technology, 69:3, 2365-2375.
40. A. Mejia, Y.R. Yue, D. Bolin, F. Lindgren, M.A. Lindquist (2020), A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis, Journal of the American Statistical Association, 115:530, 501-520.
41. D. Bolin and J. Wallin (2020), Multivariate Type G Matérn Stochastic Partial Differential Equation random fields, Journal of the Royal Statistical Society, Series B Methodology, 82:1, 215-239.
42. D. Bolin, K. Kirchner, M. Kovács (2020), Numerical solution of fractional elliptic stochastic PDEs with spatial white noise, IMA Journal of Numerical Analysis, 40:2, 1051–1073,

2019:

29. I. Stavroulaki, D. Bolin, M. Berghauser Pont and L. Marcus (2019), Statistical Modeling and Analysis of Big Data on Pedestrian Movement, Proceedings of the 12th Space Syntax Symposium.
30. E. Bobkova, L. Marcus, M. Berghauser Pont, I. Stavroulaki and D. Bolin (2019), Structure of Plot Systems and Economic Activity in Cities: Linking Plot Types to Retail and Food Services in London, Amsterdam and Stockholm, Urban Science, 3(3), 66.
31. S. Barman, H. Rootzén, D. Bolin (2019), Prediction of diffusive transport through polymer films from characteristics of the pore geometry, AiChe Journal, 65:1, 446-457.
32. D. Bolin, J. Wallin, and F. Lindgren (2019), Latent Gaussian random field mixture models, Computational statistics and data analysis, 130, 80-93.
33. Y. Yue, D. Bolin, H. Rue, and X.-F. Wang (2019), Bayesian Generalized Two-way ANOVA Modeling for Functional Data Using INLA, Statistica Sinica, 29, 741-767.

2018 and earlier:

24. P. Sidén, F. Lindgren, D. Bolin, M. Villani (2018), Efficient Covariance Approximations for Large Sparse Precision Matrices, Journal of Computational and Graphical Statistics, 27:4, 898-909
25. J. Wallin and D. Bolin (2018), Efficient adaptive MCMC through precision estimation, Journal of Computational and Graphical Statistics, 27:4, 887-897
26. D. 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.
27. A. Hildeman, D. Bolin, J. Wallin, J. Illian (2018), Level set Cox processes, Spatial Statistics, 28, 169-193.
28. 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.
29. D. Bolin and F. Lindgren (2018), Calculating probabilistic excursion sets and related quantities using excursions, Journal of Statistical Software, 86(5), 1-20.
30. 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.
31. S. Barman and D. Bolin (2018), A three-dimensional statistical model for imaged microstructures of porous polymer films, Journal of Microscopy, 269: 247–258.
32. P. Sidén, A. Eklund, D. Bolin, M. Villani (2017), Fast Bayesian whole-brain fMRI analysis with spatial 3D priors, NeuroImage, 146, 211–225.
33. D. Bolin and F. Lindgren (2017), Quantifying the uncertainty of contour maps, Journal of Computational and Graphical Statistics, 26:3, 513-524.
34. 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.
35. 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.
36. D. Bolin and J. Wallin (2016), Spatially adaptive covariance tapering, Spatial Statistics, 18, 163-178.
37. 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.
38. J. Wallin and D. Bolin (2015), Geostatistical Modelling Using Non-Gaussian Matérn Fields, Scandinavian Journal of Statistics, 42,3, 872-890.
39. 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.
40. 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.
41. 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.
42. 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.
43. D. Bolin (2014), Spatial Matérn fields driven by non-Gaussian noise, Scandinavian Journal of Statistics, 41:3, 557-579.
44. 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.
45. 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.
46. 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.
47. 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.

Theses, book chapters and other publications

• D. Bolin (2022) Spatial field, in Wiley StatsRef: Statistics Reference Online
• D. Bolin (2013), Spatial statistik och beräkningsintensiva metoder, Qvintensen Nr. 3.
• D. Bolin (2012), Models and Methods for Random Fields in Spatial Statistics with Computational Efficiency from Markov Properties, Doctoral Theses in Mathematical Sciences 2012:2, Lund University.
• D. Bolin (2011), Discussion of "An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic partial differential equation approach" by Finn Lindgren, Håvard Rue and Johan Lindström, Journal of the Royal Statistical Society, Series B, 73, 467-468.
• D. Bolin (2009), Computationally efficient methods in spatial statistics, applications in environmental modeling, Licentiate Theses in Mathematical Sciences 2009:4, Lund University.

Technical reports

• A. 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)

See David Bolin's Google scholar page for some citation info.