| Geostatistical modelling using non‐Gaussian Matérn fields J Wallin, D Bolin Scandinavian Journal of Statistics 42 (3), 872-890, 2015 | 59 | 2015 |
| Statistical models for the speed prediction of a container ship W Mao, I Rychlik, J Wallin, G Storhaug Ocean engineering 126, 152-162, 2016 | 29 | 2016 |
| BayesFlow: latent modeling of flow cytometry cell populations K Johnsson, J Wallin, M Fontes BMC bioinformatics 17 (1), 1-16, 2016 | 19 | 2016 |
| Multivariate type G Matérn stochastic partial differential equation random fields D Bolin, J Wallin Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2020 | 11 | 2020 |
| Spatially adaptive covariance tapering D Bolin, J Wallin Spatial Statistics 18, 163-178, 2016 | 11 | 2016 |
| Generalized bounds for active subspaces MT Parente, J Wallin, B Wohlmuth Electronic Journal of Statistics 14 (1), 917-943, 2020 | 10 | 2020 |
| Predation by avian insectivores on caterpillars is linked to leaf damage on oak (Quercus robur) B Gunnarsson, J Wallin, J Klingberg Oecologia 188 (3), 733-741, 2018 | 9 | 2018 |
| Maximizing leave-one-out likelihood for the location parameter of unbounded densities K Podgórski, J Wallin Annals of the Institute of Statistical Mathematics 67 (1), 19-38, 2015 | 8 | 2015 |
| Linear mixed-effects models for non-gaussian repeated measurement data Ö Asar, D Bolin, PJ Diggle, J Wallin arXiv preprint arXiv:1804.02592, 2018 | 6 | 2018 |
| Convolution-invariant subclasses of generalized hyperbolic distributions K Podgórski, J Wallin Communications in Statistics-Theory and Methods 45 (1), 98-103, 2016 | 6 | 2016 |
| Slepian noise approach for gaussian and Laplace moving average processes K Podgorski, I Rychlik, J Wallin Extremes 18 (4), 665-695, 2015 | 6 | 2015 |
| A Bayesian method to localize lost gamma sources A Bukartas, R Finck, J Wallin, CL Rääf Applied Radiation and Isotopes 145, 142-147, 2019 | 5 | 2019 |
| Level set Cox processes A Hildeman, D Bolin, J Wallin, JB Illian Spatial statistics 28, 169-193, 2018 | 5 | 2018 |
| Whole-brain substitute CT generation using Markov random field mixture models A Hildeman, D Bolin, J Wallin, A Johansson, T Nyholm, T Asklund, J Yu arXiv preprint arXiv:1607.02188, 2016 | 5 | 2016 |
| Latent Gaussian random field mixture models D Bolin, J Wallin, F Lindgren Computational Statistics & Data Analysis 130, 80-93, 2019 | 4 | 2019 |
| Modeling extreme loads acting on steering components using driving events R Maghsood, I Rychlik, J Wallin Probabilistic Engineering Mechanics 41, 13-20, 2015 | 4 | 2015 |
| Linear mixed effects models for non‐Gaussian continuous repeated measurement data Ö Asar, D Bolin, PJ Diggle, J Wallin Journal of the Royal Statistical Society: Series C (Applied Statistics) 69 …, 2020 | 3 | 2020 |
| The strong screening rule for SLOPE J Larsson, M Bogdan, J Wallin arXiv preprint arXiv:2005.03730, 2020 | 3 | 2020 |
| Scale dependence: Why the average CRPS often is inappropriate for ranking probabilistic forecasts D Bolin, J Wallin arXiv preprint arXiv:1912.05642, 2019 | 3 | 2019 |
| Efficient adaptive MCMC through precision estimation J Wallin, D Bolin Journal of Computational and Graphical Statistics 27 (4), 887-897, 2018 | 3 | 2018 |
