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.
Marie-Colette van Lieshout
September 2023