D.Sc. (dr.scient.),
Aalborg University, January 2000.
Ph.D. (lic.scient.) in statistics, University of Aarhus, December
1988.
M.Sc. (cand.scient.), majoring in statistics, minoring in
mathematics, University of Aarhus, June 1984.
Current research interests: Statistics and applied probability,
particularly spatial statistics,
stochastic geometry, simulation-based inference, Markov chain Monte
Carlo (MCMC) methods, and perfect simulation.
Formerly, Associate Editor at Advances in Applied
Probability, the Annals of Applied Probability,
the Scandinavian Journal of Statistics, and Bernoulli.
Formerly, National Editor at
the Scandinavian Journal of Statistics.
Initiator and responsible for the accreditation of the Bachelor
and Master study
in Mathematics and Technology at Aalborg University started in
September 2013.
Received the Spar
Nord Foundation's Research Prize, 2001 (honoured by 250,000 DKK).
According to an Essential Science Indicators (ESI) analysis,
over the last 10 years at October 2007 I was
among
the 1% most cited researchers in the top 50% of journals in
Mathematics, and within this group of mathematicians,
for the October 2007 - December 2007 update period, I
achieved the highest
percent increase in total citations.
In 2008, Thomson Reuters
therefore selected me as a Rising Star in the field of Mathematics
(click here for the interview by ScienceWatch and here for the interview by Nordjyske
Stiftstidende).
Comments and corrections to "Statistical Inference and Simulation
for Spatial Point Processes": click here.
2018:
R.D. Jacobsen, J. Møller, M. Nielsen and M.G. Rasmussen. Investigations of the effects of random sampling patterns on the stability of generalized sampling. To appear in Applied and Computational Harmonic Analysis. Available at arXiv: 1607.04424.
J. Møller, M. Nielsen, E. Porcu and E. Rubak. Determinantal point process models on the sphere. Bernoulli, 24, 1171-1201 (download: click here). DOI: 10.3150/16-BEJ896.
Available at arXiv: 1607.03675.
2017:
J. Møller and A.D. Christoffersen. Pair correlation functions and limiting distributions of iterated cluster point processes. Submitted
for journal publication. Available at arXiv: 1711.08984. Research Report
11, 2017, Centre for Stochastic Geometry and Advanced Bioimaging.
R.D. Jacobsen and J. Møller.
Frequentist and Bayesian inference for Gaussian–log-Gaussian wavelet trees and statistical signal processing applications.
STAT, 6, 248–256. DOI: 10.1002/sta4.156. Available at arXiv: 1405.0379.
E. Anderes, J. Møller and J.G. Rasmussen. Isotropic covariance functions on graphs and their edges. Submitted
for journal publication. Available at arXiv: 1710.01295. Research Report
10, 2017, Centre for Stochastic Geometry and Advanced Bioimaging.
J.-F. Coeurjolly, J. Møller and R. Waagepetersen. Palm distributions for log Gaussian Cox processes. Scandinavian Journal of Statistics, 44, 192–203. DOI: 10.1111/sjos.12248.
Available at arXiv: 1506.04576.
J.-F. Coeurjolly, J. Møller and R. Waagepetersen. A tutorial on Palm distributions for spatial point processes. International Statistical Review, 85, 404-420. Available at arXiv: 1512.05871.
J. Møller and R. Waagepetersen. Some recent developments in statistics for spatial point patterns. Annual Review of Statistics and Its Applications, 4, 317-342.
2016:
J. Møller, F. Safavimanesh and J.G. Rasmussen. The cylindrical
K-function and Poisson line cluster point processes. Biometrika, 103, 937-954.
DOI: 10.1093/biomet/asw044.
(Download: click here.)
C.A.N. Biscio and J. Møller. The accumulated persistence function, a new useful functional summary statistic for topological data analysis, with a view to brain artery trees and spatial point process applications.
Available at arXiv: 1611.00630.
A.H.Rafati, F. Safavimanesh, G. Wegener, M. Ardalan, B. Elfing, A.A. Mathe, J.B. Rasmussen, J. Møller, E.B.V. Jensen and J.R. Nyengaard. The Effect of Maternal Separation on the Neurovascular Alteration of Medial Prefrontal Cortex and Serum Level of Brain-derived Neurotrophic Factor in a Genetic Rat Model of Depression.
Submitted for journal publication.
J. Møller and E. Rubak. Functional summary statistics on the sphere with an application to determinantal point processes. Spatial Statistics, 18, 4-23. DOI: 10.1016/j.spasta.2016.06.004.
J. Møller and E. Rubak. Determinantal point processes and functional summary statistics on the sphere. Research Report
2, 2016, Centre for Stochastic Geometry and Advanced Bioimaging.
J. Møller, M. Ghorbani and E. Rubak. Mechanistic spatio-temporal point process models
for marked point processes, with a view to forest
stand data. Biometrics, 72, 687-696. DOI: 10.1111/biom.12466.
F. Lavancier and J. Møller. Modelling aggregation on the large
scale and regularity on the small scale in spatial point pattern
datasets. Scandinavian Journal of Statistics, 43,
587-609. DOI: 10.1111/sjos.12193.
A.H. Rafati, F. Safavimanesh, K.-A. Dorph-Petersen,
J.G. Rasmussen, J. Møller and J.R. Nyengaard. Detection and spatial characterization of minicolumnarity in
the human cerebral cortex. Journal of Microscopy, 261, 115-126. DOI: 10.1111/jmi.12321.
2015:
J.-F. Coeurjolly, J. Møller and R. Waagepetersen. Conditioning
in spatial point processes. Research Report 14, 2015, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv: 1512.05871.
J. Møller, M. Nielsen, E. Porcu and E. Rubak. Determinantal point process models on the sphere. Research Report
13, 2015, Centre for Stochastic Geometry and Advanced Bioimaging.
J.-F. Coeurjolly, J. Møller and R. Waagepetersen. Palm distributions for log Gaussian Cox processes. Research Report
9, 2015, Centre for Stochastic Geometry and Advanced Bioimaging.
J. Møller, F. Safavimanesh and J.G. Rasmussen. The cylindrical
K-function and Poisson line cluster point processes. Research Report
3, 2015, Centre for Stochastic Geometry and Advanced Bioimaging. Available at arXiv: 1503.07423.
F. Lavancier, J. Møller and E. Rubak. Determinantal point process models and statistical
inference. Journal of Royal Statistical
Society: Series B (Statistical Methodology), 77,
853-877. Doi 10.1111/rssb.12096.
V. Suryaprakash, J. Møller and G.P. Fettweis.
On the modeling and analysis of
heterogeneous radio access networks using
a Poisson cluster process.
IEEE Transactions on Wireless Communications, 14, 1035-1047. DOI 10.1109/TWC.2014.2363454.
J. Møller and J.G. Rasmussen. Spatial cluster point processes related to
Poisson-Voronoi tessellations. Stochastic Environmental
Research and Risk Assessment, 29, 431-441. DOI
10.1007/s00477-014-0914-3.
R. Fierro, V. Leiva and J. Møller. The Hawkes process with
different excitation functions and its asymptotic behaviour.
Journal in Applied Probability, 52, 37-54. DOI 10.1017/S0021900200012183.
J. Møller and M. Ghorbani.
Functional summary statistics for the Johnson-Mehl model. Journal
of Statistical Computation and Simulation, 85, 899-916. DOI
10.1080/00949655.2013.850691.
J. Møller and D. Stoyan. Stochastic geometry and random tessellations.
To appear in Tessellations in the Sciences:
Virtues, Techniques and Applications of Geometric Tilings,
Eds. R. van de Weijgaert, G. Vegter,
V. Icke and J. Ritzerveld, Springer-Verlag.
2014:
F. Lavancier, J. Møller and E. Rubak. Determinantal point process models and statistical
inference: Extended version. Available at arXiv: 1205.4818.
J. Møller, M. Ghorbani and E. Rubak. Mechanistic spatio-temporal point process models
for marked point processes, with a view to forest
stand data.
Research Report R-2014-07,
Department of Mathematical Sciences,
Aalborg University.
P.G.M. Forbes, S. Lauritzen and J. Møller.
Fingerprint analysis with marked point processes.
Research Report R-2014-06,
Department of Mathematical Sciences,
Aalborg University. Submitted for journal publication.
J. Møller and R.D. Jacobsen.
Gaussian-log-Gaussian wavelet trees, frequentist
and Bayesian inference, and statistical signal
processing applications.
Research Report R-2014-04,
Department of Mathematical Sciences,
Aalborg University.
T.S. Nielsen, J.R. Nyengaard, J. Møller, J. Banner, L.P. Nielsen
and U.T. Baandrup. Quantitative diagnosis of lymphocytic myocarditis
in forensic medicine. Forensic Science International, 238, 9-15. DOI: 10.1016/j.foresciint.2014.02.012.
J. Møller and H. Toftager. Geometric anisotropic spatial point pattern
analysis and Cox processes. Scandinavian Journal of Statistics,
41, 414-435. DOI 10.1111/sjos.12041.
J.-F. Coeurjolly and J. Møller. Variational approach for spatial
point process intensity estimation. Bernoulli, 20, 1097-1125. DOI: 10.3150/13-BEJ516.
2013:
R. Fierro, V. Leiva and J. Møller. The Hawkes process with
different excitation functions and its asymptotic behaviour.
Research Report R-2013-14,
Department of Mathematical Sciences,
Aalborg University.
J. Møller and J.G. Rasmussen. Spatial cluster point processes related to
Poisson-Voronoi tessellations. Research Report R-2013-10, Department of Mathematical Sciences,
Aalborg University.
J. Møller and M. Ghorbani.
Functional summary statistics for the Johnson-Mehl model.
Research Report R-2013-01, Department of Mathematical Sciences,
Aalborg University.
2012:
J.-F. Coeurjolly and J. Møller.
Variational approach for spatial point process
intensity estimation.
Research Report R-2012-04, Department of Mathematical Sciences,
Aalborg University.
F. Lavancier, J. Møller and E. Rubak. Statistical aspects of determinantal point processes. Research Report R-2012-02, Department of Mathematical Sciences,
Aalborg University.
J. Møller. Contribution to the
discussion of Fearnhead, P., and
Prangle, D. (2012): Constructing summary statistics for approximate
Bayesian computation: semi-automatic approximate Bayesian
computation.
Journal of Royal Statistical
Society: Series B (Statistical Methodology), 74,
465.
J. Møller and M. Ghorbani. Aspects of second-order analysis of structured
inhomogeneous spatio-temporal point processes. Statistica
Neerlandica, 66, 472-491.
J. Møller and K.K. Berthelsen. Transforming spatial point
processes into Poisson processes using random superposition.
Advances in Applied
Probability, 44, 42-64.
J. Møller and J.G. Rasmussen.
A sequential point process model and Bayesian inference for
spatial point patterns with linear structures.
Scandinavian Journal of Statistics, 39, 618-634.
J. Møller and H. Toftager. Geometric anisotropic spatial point pattern
analysis and Cox processes. Research Report R-2012-01, Department of Mathematical Sciences,
Aalborg University.
J. Møller. Contribution to the
discussion of Lindgren, F., Rue, H. and
Lindström, J. (2011):
An explicit link between Gaussian
fields and Gaussian Markov random fields: The stochastic partial
differential equation approach. Journal of Royal Statistical
Society: Series B (Statistical Methodology), 73,
466-467.
M.L. Yiu, C.S. Jensen, J. Møller and H. Lu. Design and Analysis of a Ranking Approach to Private
Location-Based Services. ACM Transactions on Database
Systems (TODS), 36(2): Article 10, 42 pages.
J. Møller and M.L. Yiu. Probabilistic results for a
mobile service scenario. Advances in Applied
Probability, 43, 322-334.
2010:
J. Møller and M. Ghorbani. Second-order analysis of structured
inhomogeneous spatio-temporal point processes. Research Report R-2010-11, Department of Mathematical Sciences,
Aalborg University.
E. Rubak, J. Møller and P. McCullagh. Statistical inference for a
class of multivariate negative binomial distributions.
Research Report R-2010-10, Department of Mathematical Sciences,
Aalborg University. Submitted for journal publication.
J. Møller and K.K. Berthelsen. Transforming spatial point
processes into Poisson processes using random superposition.
Research Report R-2010-09, Department of Mathematical Sciences,
Aalborg University.
J. Møller and J.G. Rasmussen.
A sequential point process model and Bayesian inference for
spatial point patterns with linear structures. Research Report R-2010-08, Department of Mathematical Sciences,
Aalborg University.
A. Baddeley, E. Rubak and J. Møller. Score, pseudo-score and
residual diagnostics for
goodness-of-fit of spatial point
process models. Research Report R-2010-06, Department of Mathematical Sciences,
Aalborg University.
J. Møller and M.L. Yiu. Probabilistic results for a
mobile service scenario. Research Report R-2010-03, Department of Mathematical Sciences,
Aalborg University.
J. Møller, M. L. Huber and R. L. Wolpert. Perfect simulation and
moment properties for the Matérn type III process. Stochastic
Processes and their Applications, 120, 2142-2158.
Brian H. Aukema, Jun Zhu, Jesper Møller, Jakob G. Rasmussen, and Kenneth F. Raffa.
Predisposition to bark beetle attack by root herbivores and associated
pathogens: Roles in forest decline, gap formation, and persistence
of endemic bark beetle populations. Forest Ecology
and Management, 259, 374-382.
J. Møller. Inference. Chapter 9 in New Perspectives in Stochastic Geometry,
Eds. W.S. Kendall and I. Molchanov, Oxford University Press,
Oxford, 307-347.
J. Møller and R.P. Schoenberg. Thinning spatial point processes
into Poisson processes. Advances in Applied
Probability, 42, 347-358.
J. Møller. Parametric methods. Chapter 19 in
A Handbook of Spatial Statistics.
Eds. A.E. Gelfand, P. Diggle, M. Fuentes, and P. Guttorp, Chapman
and Hall/CRC Press, 317-337.
J. Møller and Carlos Diaz-Avalos. Structured
spatio-temporal shot-noise Cox point process
models, with a view to modelling forest fires.
Scandinavian Journal of
Statistics, 37, 2-25.
J. Møller and K. Helisova. Likelihood inference for unions
of interacting discs. Scandinavian Journal of
Statistics, 37, 365-381.
J. Møller and Ege Rubak. A model for positively correlated count
variables. International Statistical Review, 78, 65-80.
2009:
J. Møller, M. L. Huber and R. L. Wolpert. Perfect simulation and moment properties for the Matérn type III process. Research Report R-2009-12, Department of Mathematical Sciences, Aalborg University.
J. Møller and M.L. Yiu. The distribution of communication cost for a
mobile service scenario. Research Report R-2009-11, Department of Mathematical Sciences,
Aalborg University.
J. Møller and K. Helisova. Simulation of random set models for unions of discs and the use of power
tessellations. The Sixth
International Symposium on Voronoi Diagrams in Science and
Engineering, Ed. F. Anton, IEEE Computer Socity, Los Alamitos,
California, 99-108.
K. Helisova and J. Møller. Model pro nahodne
sjednoceni kruhu se vzajemnymi interakcemi. Proceedings of
the ROBUST '08 conference, Eds. J. Antoch and G. Dohnal, JCMF,
Prague, 89-96.
K. Helisova and J. Møller. Model for random union of interacting
discs. Stereology and Image Analysis. Ecs10: Proceedings of The
10th European Congress of ISS, Eds. V. Capasso et al., The
MIRIAM Project Series, Vol. 4, ESCULAPIO Pub. Co., Bologna,
Italy, 437-441.
J. Møller and R.P. Schoenberg. Thinning spatial point processes
into Poisson processes. Research Report R-2009-01,
Department of Mathematical Sciences, Aalborg University.
J. Møller and J.G. Rasmussen.
Modelling point patterns with linear
structures.Stereology and Image Analysis. Ecs10: Proceedings of The
10th European Congress of ISS, Eds. V. Capasso et al., The
MIRIAM Project Series, Vol. 4, ESCULAPIO Pub. Co., Bologna,
Italy, 273-278.
J.B. Illian, J. Møller and R.P. Waagepetersen. Hierarchical spatial point process analysis for a plant community with high biodiversity.
Environmental and Ecological Statistics, 16, 389-405.
2008:
J. Møller and K. Helisova. Likelihood inference for unions
of interacting discs.
Research Report
R-2008-18, Department of Mathematical Sciences, Aalborg
University.
J. Møller and Ege Rubak. Properties and simulation of alpha-permanental random fields.
Research Report
R-2008-13, Department of Mathematical Sciences, Aalborg
University.
J. Møller and Carlos Diaz-Avalos. Structured
spatio-temporal shot-noise Cox point process
models, with a view to modelling forest fires.
Research Report
R-2008-07, Department of Mathematical Sciences, Aalborg
University.
J. Møller. Parametric methods for spatial point processes.
Research Report
R-2008-04, Department of Mathematical Sciences, Aalborg
University.
J. Møller and K. Helisova. Power diagrams and interaction
processes for unions of discs.
Advances in Applied Probility, 40, 321-347.
K.K. Berthelsen and J. Møller. Non-parametric Bayesian
inference for inhomogeneous Markov point processes.
Australian and New Zealand Journal of Statistics, 50, 257-272.
J. Zhu, J.G. Rasmussen, J. Møller, B.H. Aukema and K.F. Raffa. Spatial-temporal modeling of forest gabs generated by
colonization from below- and above-ground bark
beetle species.
Journal of the American Statistical Association, 103, 162-177.
A. Baddeley, J. Møller and A.G. Pakes. Properties of
residuals for spatial point processes. Annals of the Institute of Statistical
Mathematics, 60, 627-649.
J. Møller. Contribution to the
discussion of P. McCullagh (2008): Sampling
bias and logistic models. Journal of Royal Statistical
Society: Series B (Statistical Methodology), 70, 669.
2007:
J. Møller. Inference. Research Report
R-2007-22, Department of Mathematical Sciences, Aalborg
University.
J. Møller and D. Stoyan. Stochastic geometry and random tessellations.
Research Report
R-2007-28, Department of Mathematical Sciences, Aalborg
University.
J. Møller and K. Helisova. Power diagrams and interaction
processes for unions of discs.
Research Report
R-2007-15, Department of Mathematical Sciences, Aalborg
University.
J.G. Rasmussen, J. Møller, B.H. Aukema,
K.F. Raffa and J. Zhu. Continuous time modelling of
dynamical spatial lattice data observed at sparsely distributed
times. Journal of Royal Statistical Society: Series B (Statistical Methodology), 69, 701-713.
J. Møller and R.P. Waagepetersen. Modern statistics for spatial point processes
(with discussion). Scandinavian Journal of Statistics, 34,
643-711.
J. Møller and G.L. Torrisi.
The pair correlation function of spatial Hawkes processes.
Statistics and Probability Letters, 77, 995-1003.
J. Møller and K. Mengersen. Ergodic averages for monotone
functions via dominating processes.
Bayesian Statistics 8, Eds. J.M. Bernardo,
M.J. Bayarri,
J.O. Berger, A.P. Dawid, D. Heckerman,
A.F.M. Smith and M. West, Oxford University Press, 643-648.
J. Møller and K. Mengersen. Ergodic averages using upper and lower
dominating processes. Bayesian Analysis, 2, 761-782.
Ø. Skare, J. Møller and E.B.V. Jensen. Bayesian analysis of
spatial point processes in the neighbourhood of Voronoi networks.
Statistics and Computing, 17, 369-379.
2006:
P. McCullagh and J. Møller. The permanental process.
Advances in Applied Probability, 38, 873-888.
J.G. Rasmussen, J. Møller, B.H. Aukema,
K.F. Raffa and J. Zhu. Bayesian inference for multivariate point processes observed at sparsely distributed
times. Research Report
R-2006-24, Department of Mathematical Sciences, Aalborg
University.
J. Møller and R.P. Waagepetersen. Modern statistics for spatial point processes. Research Report
R-2006-12, Department of Mathematical Sciences, Aalborg
University.
J.B. Illian, J. Møller and R.P. Waagepetersen. Spatial point process analysis for a plant community with high biodiversity. Research Report
R-2006-05, Department of Mathematical Sciences, Aalborg
University.
J. Zhu, J.G. Rasmussen, J. Møller, B.H. Aukema and K.F. Raffa. Spatial-temporal modeling of forest gabs generated by
colonization from below- and above-ground bark
beetle species. Research Report
R-2006-04, Department of Mathematical Sciences, Aalborg
University.
A. Baddeley, J. Møller and A.G. Pakes. Properties of
residuals for spatial point processes. Research Report
R-2006-03, Department of Mathematical Sciences, Aalborg
University.
Ø. Skare, J. Møller and E.B.V. Jensen. Bayesian analysis of
spatial point processes in the neighbourhood of Voronoi networks. Research Report
R-2006-02, Department of Mathematical Sciences, Aalborg
University.
J. Møller and K. Mengersen. Ergodic averages for monotone
functions using upper and lower dominating processes. Research Report
R-2006-01, Department of Mathematical Sciences, Aalborg
University.
J. Møller, A.N. Pettitt, K.K. Berthelsen and R.W. Reeves. An efficient Markov chain Monte Carlo method for
distributions with intractable normalising
constants. Biometrika, 93, 451-458. (Download: click here.)
J. Møller and J.G. Rasmussen. Approximate simulation of Hawkes processes.
Methodology
and Computing in Applied Probability, 8, 53-64.
J. Møller. Contribution to the
discussion of A. Beskos, O. Papaspiliopoulos, G.O. Roberts and
P. Fearnhead (2006): Exact and computationally efficient
likelihood-based estimation for discretely observed diffusion
processes. Journal of Royal Statistical
Society: Series B (Statistical Methodology), 68, 373.
K.K. Berthelsen and J. Møller. Bayesian analysis of Markov
point processes. In Case Studies in Spatial Point Process
Modeling (Eds. A. Baddeley,
P. Gregori, J. Mateu, R. Stoica and D.
Stoyan), Springer Lecture Notes in Statistics 185,
Springer-Verlag, New York, 85-97.
2005:
J. Møller. Properties of spatial Cox process models.
Journal of Statistical Research of Iran, 2, 1-18.
V. Benes, K. Bodlak, J. Møller and R.P. Waagepetersen. A case
study on point process modelling in disease mapping. Image Analysis and Stereology, 24, 159-168.
P. McCullagh and J. Møller. The permanent process. Research Report
R-2005-29, Department of Mathematical Sciences, Aalborg
University.
J. Møller and G.L. Torrisi. Second order analysis for
spatial Hawkes processes. Research Report
R-2005-20, Department of Mathematical Sciences, Aalborg
University.
A. Baddeley, R. Turner, J. Møller and
M. Hazelton. Residual analysis for spatial point
processes (with discussion). Journal of Royal Statistical
Society: Series B (Statistical Methodology), 67, 617-666.
J. Møller and J.G. Rasmussen. Perfect simulation of Hawkes
processes. Advances in Applied Probability, 37, 629-646.
J. Møller and G.L. Torrisi. Generalised shot noise Cox
processes. Advances in Applied Probability, 37, 48-74.
2004:
J. Møller and R.P. Waagepetersen. Statistical Inference and
Simulation for Spatial Point Processes. Chapman and Hall/CRC,
Boca Raton.
J. Møller and J.G. Rasmussen. Approximate simulation of Hawkes processes. Research Report
R-2004-28, Department of Mathematical Sciences, Aalborg
University.
K.K. Berthelsen and J. Møller. Bayesian analysis of Markov point processes. Research Report
R-2004-25, Department of Mathematical Sciences, Aalborg
University.
G. Döge, K. Mecke, J. Møller, D. Stoyan and
R.P. Waagepetersen. Grand canonical simulations of hard-disk
systems by simulated tempering. International Journal of
Modern Physics C, 15, 129-147.
K.K. Berthelsen and J. Møller. A short diversion into the theory of
Markov chains,
with a view to Markov chain Monte Carlo methods. Internal Report
R-2004-01, Department of Mathematical Sciences, Aalborg University.
A. Baddeley, R. Turner, J. Møller and
M. Hazelton. Residual analysis for spatial point
processes. Research Report 2004/08, School of Mathematics and Statistics, University of
Western Australia. Version
submitted for JRSS B.
J. Møller and G.L. Torrisi. Generalised shot noise Cox
processes. Research Report
R-2004-07, Department of Mathematical Sciences, Aalborg
University.
J. Møller, A.N. Pettitt, K.K. Berthelsen and R.W. Reeves. An efficient Markov chain Monte Carlo method for
distributions with intractable normalising constants. Research Report
R-2004-02, Department of Mathematical Sciences, Aalborg
University.
2003:
K.K. Berthelsen and J. Møller. Likelihood and
non-parametric Bayesian MCMC inference for spatial point processes
based on perfect simulation and path sampling. Scandinavian
Journal of Statistics, 30, 549-564.
J. Møller. Shot noise Cox processes. Advances
in Applied Probability, 35, 614-640.
V. Benes, K.
Bodlak,
J. Møller and R. Waagepetersen. Application of log Gaussian Cox
processes in disease mapping. Proceedings of the ISI
Conference
on Environmental Statistics and Health, Santiago de
Compostela,
2003. Eds. J. Mateu, D. Holland and W.
Gonzalez-Manetiga,
95-105.
J. Møller. A comparison of spatial point process models in
epidemiological aplications. Highly Structured Stochastic Systems, Eds. P.J. Green,
N.L. Hjort and S. Richardson, Oxford University Press, 264-268.
J. Møller (editor). Spatial Statistics and Computational
Methods,
Lecture Notes in Statistics 173, Springer-Verlag, New York.
J. Møller and R.P. Waagepetersen. An introduction to simulation-based inference for spatial point processes.
In J. Møller, Spatial Statistics and Computational Methods,
Lecture Notes in Statistics 173, Springer-Verlag, New York,
143-198.
J. Møller. Contribution to the discussion of S.P. Brooks,
P. Giudici
and G.O. Roberts: Efficient constructions of reversible
jump Markov chain Monte Carlo proposal distributions. Journal
of Royal Statistical Society: Series B (Statistical Methodology), 65, 42-43.
P.G. Blackwell and J. Møller. Bayesian analysis of deformed
tessellation models. Advances in Applied Probability,
35, 4-26.
2002:
K.K. Berthelsen and J. Møller. A primer on perfect simulation for spatial point processes. Bulletin of the Brazilian Mathematical
Society, 33, 351-367.
J. Møller and R.P. Waagepetersen. Statistical inference for Cox
processes. Spatial Cluster Modelling, Eds. Andrew B. Lawson
and David Denison, Chapman and Hall/CRC, 37-60.
V. Benes, K. Bodlak, J. Møller and R.P. Waagepetersen. Bayesian
analysis of log Gaussian Cox process models for disease mapping. Research Report
R-02-2001, Department of Mathematical Sciences, Aalborg
University.
K.K. Berthelsen and J. Møller. Spatial jump
processes and perfect simulation. Morphology of Condensed Matter,
Eds. K. Mecke and D. Stoyan, Lecture Notes in Physics, Vol. 600,
Springer-Verlag, 391-417.
M.B. Hansen, J. Møller and F.Aa. Tøgersen.
Bayesian contour detection in a time series of ultrasound images
through dynamic deformable template models. Biostatistics,
3, 213-228.
A. Mira, J. Møller and G.O. Roberts. Corrigendum: perfect slice samplers. Journal of Royal Statistical
Society: Series B (Statistical Methodology), 64, 581.
2001:
S. Mase, J. Møller, D. Stoyan, R.P. Waagepetersen and
G. Döge. Packing densities and simulated tempering for hard core Gibbs
point processes. Annals of the Institute of Statistical
Mathematics, 53, 661-680.
J. Møller. A review of perfect simulation in stochastic
geometry. Selected Proceedings of the Symposium on Inference for
Stochastic Processes, Eds. I.V. Basawa, C.C. Heyde and
R.L. Taylor, IMS Lecture Notes & Monographs Series, 2001, Volume
37, pages 333-355.
A. Brix and J. Møller. Space-time multi type log Gaussian Cox
processes with a view to modelling weed data. Scandinavian Journal of Statistics, 28, 471-488.
O.F. Christensen, J. Møller and R.P. Waagepetersen. Geometric
ergodicity of Metropolis-Hastings algorithms for conditional
simulation in generalised linear mixed models.
Methodology and Computating in Applied Probability, 3, 309-327.
A. Mira, J. Møller and G.O. Roberts. Perfect slice samplers. Journal of Royal Statistical
Society: Series B (Statistical Methodology), 63, 593-606.
A. Mira, J. Møller and G. O. Roberts.
Perfect simple slice samplers. In Bulletin of the International Statistical Institute,
53rd Session Proceedings}, Tome LIX, Book 1, 73-79.
J. Møller and Ø. Skare. Coloured Voronoi
tessellations for Bayesian image analysis and reservoir modelling.
Statistical Modelling, 1, 213-232.
2000:
A. Baddeley, J. Møller and R. Waagepetersen. Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica, 54, 329-350.
O.F. Christensen, J. Møller and R.P. Waagepetersen. Analysis of spatial data using generalized linear mixed models and Langevin-type Markov chain Monte Carlo. Research Report R-00-2009, Department of Mathematical Sciences, Aalborg University.
G. Döge, K. Mecke, J. Møller, D. Stoyan and R.P. Waagepetersen. Grand canonical simulations of hard-disk systems by simulated tempering. Research Report R-00-2003, Department of Mathematical Sciences, Aalborg University.
W.S. Kendall and J. Møller. Perfect simulation using dominating processes on ordered spaces,
with application to locally stable point processes. Advances in Applied Probability, 32, 844-865.
J. Møller. Aspects of Spatial Statistics, Stochastic Geometry and Markov Chain Monte Carlo, D.Sc. thesis accepted by the Faculty of Engineering and Science, Aalborg University.
1999:
O. Häggström, M.N.M. van Lieshout and J. Møller. Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes. Bernoulli, 5, 641-659.
W.S. Kendall and J. Møller. Perfect Metropolis-Hastings simulation of locally stable point processes. Research Report R-99-2001, Department of Mathematical Sciences, Aalborg University.
W.S. Kendall and J. Møller. Perfect implementation of a Metropolis-Hastings simulation of Markov point processes. Research Report 348, Department of Statistics, University of Warwick.
J. Møller. Notes on Markov chain Monte Carlo methods. Lecture Notes, Department of Mathematical Sciences, Aalborg University.
J. Møller. Markov chain Monte Carlo and spatial point processes. In Stochastic Geometry: Likelihood and Computations, Eds. O.E. Barndorff-Nielsen, W.S. Kendall and M.N.M. van Lieshout, Monographs on Statistics and Applied Probability, Boca Raton, Chapman and Hall/CRC, 141-172.
J. Møller. Topics in Voronoi and Johnson-Mehl tessellations. In Stochastic Geometry: Likelihood and Computations, Eds. O.E. Barndorff-Nielsen, W.S. Kendall and M.N.M. van Lieshout, Monographs on Statistics and Applied Probability, Boca Raton, Chapman and Hall/CRC, 173-198.
J. Møller. Perfect simulation of conditionally specified models. Journal of Royal Statistical
Society: Series B (Statistical Methodology), 61, 251-264.
J. Møller and G. Nicholls. Perfect simulation for sample-based
inference. Research Report
R-99-2011, Department of Mathematical Sciences, Aalborg
University. To appear in Statistics and Computing
(conditionally accepted).
J. Møller and R.P. Waagepetersen. Contribution to the discussion
of Besag, J. and Higdon, D. (1999): Bayesian analysis of
agricultural field experiments. Journal of the Royal Statistical
Society: Series B (Statistical Methodology), 61, 735.
J. Møller and K. Schladitz. Extensions of Fill's algorithm for
perfect simulation. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61, 955-969.
Last modified: Sun Feb 01 14:37:28 Romance Standard Time 2009