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.
• 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. 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.
• M N M van Lieshout. Contribution to the Discussion of "Marked Spatial Point Processes: Current State and Extensions to Point Processes on Linear Networks" by Eckardt and Moradi. Journal of Agricultural, Biological and Environmental Statistics, to appear.
• M N M van Lieshout and R L Markwitz. A non-homogeneous semi-Markov model for interval censoring. Arxiv 2401.17905, January 2024.
• C Lu, Y Guan, M N M van Lieshout and G Xu. XGBoostPP: Tree-based estimation of point process intensity functions. Arxiv 2401.17966, January 2024.
• Z Baki and M N M van Lieshout. On the moments of Cox rate-and-state models. Arxiv 2403.13413, March 2024.