Preprints and Forthcoming Papers

• M N M van Lieshout, Stochastic geometry models in image analysis and spatial statistics, CWI tract 108, 1995.
• O Barndorff-Nielsen, W S Kendall, and M N M van Lieshout (Eds.) Stochastic geometry, likelihood and computation, Chapman and Hall/CRC Press, 1999.
• M N M van Lieshout, Markov point processes and their applications, Imperial College Press, 2000.
• L Florack, R Duits, G Jongbloed, M-C van Lieshout and L Davies (Eds.), Mathematical methods for signal analysis and representation, Springer, 2012.
• M N M van Lieshout. Theory of spatial statistics: A concise introduction. Chapman and Hall/CRC Press, 2019.
• C Lu, M N M van Lieshout, M de Graaf and P J Visscher. Data-driven chimney fire risk prediction using machine learning and point process tools. ArXiv 2112.07257, December 2021. Annals of Applied Statistics, to appear.
• M N M van Lieshout and Z Baki. Exploring seismic hazard in the Groningen gas field using adaptive kernel smoothing. ArXiv 2209.02386, August 2022. Mathematical Geosciences, to appear.
• M N M van Lieshout and C Lu. Infill asymptotics for logistic regression estimators for spatio-temporal point processes. ArXiv 2208.12080, August 2022.
• M N M van Lieshout. Non-parametric adaptive bandwidth selection for kernel estimators of spatial intensity functions. ArXiv 2210.11902, October 2022.
• M N M van Lieshout. Optimal decision rules for marked point process models. Arxiv 2309.03752, September 2023.
• M N M van Lieshout and C Lu. Contribution to the Discussion of "Automatic Change-Point Detection in Time Series via Deep Learning" by Li, Fearnhead, Fryzlewics and Wang. Journal of the Royal Statistical Society, to appear.