Publications

Journal articles and preprints #

• Marina Struleva, Shayan Hundrieser, DS and Axel Munk (2026).
  Sharp Convergence Rates of Empirical Unbalanced Optimal Transport for Spatio-Temporal Point Processes, Stochastic Process. Appl. 198, 104938.
• Marianne Abémgnigni Njifon, Tobias Weber, Viktor Bezborodov, Tyll Krueger and DS (2025).
  Block Graph Neural Networks for tumor heterogeneity prediction, arXiv preprint.
• DS and Leoni Carla Wirth (2024). Stein's Method for Spatial Random Graphs, arXiv preprint.
• Raoul Müller, DS and Jorge Mateu (2024). ANOVA for metric spaces, with applications to spatial data,
  Statistical Science 39(2), 262-285.
• Marianne Abémgnigni Njifon and DS (2024). Graph convolutional networks for spatial interpolation of correlated data,
  Spatial Statistics 60, 100822.
• DS and Leoni Carla Wirth (2023). Assignment Based Metrics for Attributed Graphs, arXiv preprint.
• DS (2023). Distance maps between Japanese kanji characters based on hierarchical optimal transport, arXiv preprint.
• Raoul Müller, Anita Schöbel, DS (2023). Location problems with cutoff,
  Asia-Pacific Journal of Operational Research 40(3), 2250045.
• Johannes Wieditz, Yvo Pokern, DS and Stephan Huckemann (2022).
  Characteristic and necessary minutiae in fingerprints, J R Stat Soc Series C 71, 27–50.
• Garyfallos Konstantinoudis, DS, Roland Ammann, Tamara Diesch, Claudia Kuehni and Ben Spycher (2020).
  Bayesian spatial modelling of childhood cancer incidence in Switzerland using exact point data: a nationwide study during 1985–2015, International Journal of Health Geographics 19(15).
• Raoul Müller, DS and Jorge Mateu (2020). Metrics and barycenters for point pattern data,
  Statistics and Computing 30, 953-972.
• Valentin Hartmann and DS (2020).
  Semi-discrete optimal transport: a solution procedure for the unsquared Euclidean distance case,
  Mathematical Methods of Operations Research 92, 133-163.
• Fabian Kück and DS (2020). Convergence rates for the degree distribution in a dynamic network model,
  Stochastic Models 36(1), 134-171. [Published version]
• Garyfallos Konstantinoudis, DS, Håvard Rue and Ben Spycher (2020).
  Discrete versus continuous domain models for disease mapping, Spatial and Spatio-Temporal Epidemiology 32, 100319. [Published version]
• Günther Koliander, DS and Franz Hlawatsch (2018). Rate-distortion theory of finite point processes,
  IEEE Trans. Inf. Theory 64(8), 5832-5861. [Published version]
• Fabian Kück and DS (2018). On the age of a randomly picked individual in a linear birth and death process,
  J. Appl. Probab. 55(1), 82-93. [Published version]
• Jörn Schrieber, DS and Carsten Gottschlich (2017). DOTmark - a benchmark for discrete optimal transport,
  IEEE Access 5, 271-282.
• Nathan Ross and DS (2017). Wireless network signals with moderately correlated shadowing still appear Poisson,
  IEEE Trans. Inf. Theory 63(2), 1177-1198. [Published version]
• DS, Anja Sturm and Henryk Zähle (2016).
  On qualitative robustness of the Lotka-Nagaev estimator for the offspring mean of a supercritical Galton-Watson process,
  J. Stat. Plan. Inference 169, 56--70.
• Carsten Gottschlich and DS (2014).
  The shortlist method for fast computation of the earth mover's distance and finding optimal solutions to transportation problems,
  PLoS ONE 9(10), e110214.
• Lutz Dümbgen, Kaspar Rufibach and DS (2014).
  Maximum-likelihood estimation of a log-concave density based on censored data,
  Electron. J. Statist. 8, 1405-1437.
• Kaspar Stucki and DS (2014). Bounds for the probability generating functional of a Gibbs point process,
  Adv. Appl. Probab. 46(1), 21-34.
• DS and Kaspar Stucki (2014). Gibbs point process approximation: total variation bounds using Stein’s method,
  Ann. Probab. 42(5), 1911-1951
• Lutz Dümbgen, Richard Samworth and DS (2011).
  Approximation by log-concave distributions, with applications to regression, Ann. Statist. 39(2), 702--730.
  Extended version: Technical report 75, IMSV, University of Bern (http://arxiv.org/pdf/1002.3448v3).
• DS, André Hüsler and Lutz Dümbgen (2011). Multivariate log-concave distributions as a nearly parametric model,
  Statistics & Risk Modeling 28(3), 277--295.
  Original article by kind permission of Oldenbourg Wissenschaftsverlag, Munich/Germany.
  Extended version: Technical report 74, IMSV, University of Bern (http://arxiv.org/abs/0907.0250).
• A. Baddeley, M. Berman, N. Fisher, A. Hardegen, R. Milne, DS, R. Shah, and R. Turner (2010).
  Spatial logistic regression and change-of-support in Poisson point processes,
  Electron. J. Statist. 4, 1151--1201.
• DS and Lutz Dümbgen (2010). Consistency of multivariate log-concave density estimators,
  Statist. Probab. Letters 80(5-6), 376--380.
• DS (2009). Stein's method and Poisson process approximation for a class of Wasserstein metrics,
  Bernoulli 15(2), 550--568.
• DS (2009). Distance estimates for dependent thinnings of point processes with densities,
  Electron. J. Probab. 14, 1080--1116.
• DS and Aihua Xia (2008). A new metric between distributions of point processes,
  Adv. Appl. Probab. 40(3), 651--672.
• DS, Ba-Tuong Vo and Ba-Ngu Vo (2008). A consistent metric for performance evaluation of multi-object filters,
  IEEE Trans. Signal Processing 56(8, part 1), 3447--3457.
• DS (2005). Distance estimates for dependent superpositions of point processes,
  Stochastic Process. Appl. 115, 1819--1837. [Reprint of final version]
• DS (2005). Distance estimates for Poisson process approximations of dependent thinnings,
  Electron. J. Probab. 10, 165--201.
• DS (2005). Upper Bounds for Spatial Point Process Approximations,
  Ann. Appl. Probab. 15, no. 1B, 615--651.
   

Book chapters, contributions to collections/proceedings #

• David Ginsbourger, Olivier Roustant, DS, Nicolas Durrande and Nicolas Lenz (2016).
  On ANOVA decompositions of kernels and Gaussian random field paths,
  in Monte Carlo and Quasi-Monte Carlo Methods: MCQMC, Leuven, Belgium, April 2014
  Ronald Cools and Dirk Nuyens (editors), Springer Proceedings in Mathematics and Statistics 163, 315-330.
• DS (2014). Stein’s method for approximating complex distributions, with a view towards point processes,
  in: Stochastic Geometry, Spatial Statistics and Random Fields, Vol. II: Models and Algorithms,
  Volker Schmidt (editor), Springer Lecture Notes in Mathematics 2120, 1-30.
• Lutz Dümbgen, Richard Samworth and DS (2013). Stochastic search for semiparametric linear regression models,
  in: From Probability to Statistics and Back: High-Dimensional Models and Processes,
  A Festschrift in Honor of Jon Wellner, IMS collections vol. 9, 78--90.

E-book #

• DS (2026), Spatial Stochastics: A Three-Semester Course.