Homepage for Jesper Møller

Jesper Møller
Department of Mathematical Sciences
Aalborg University
Fredrik Bajers Vej 7G
DK-9220 Aalborg Ø
Phone: (+45) 96 35 88 63
Fax: (+45) 98 15 81 29
Email: jm@math.aau.dk

Briefly about me:

Professor at Department of Mathematical Sciences, Aalborg University.
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.
Head of the Doctoral School in Mathematics and Physics at the International Doctoral School of Technology and Science, Aalborg University.
Member of "The PhD Board", Faculties of Engineering, Science and Medicine, Aalborg University.
Member of "The Advisory Research Committee of the Dean", Faculties of Engineering, Science and Medicine, Aalborg University.
Member of "The Spar Nord Foundation's Research Prize Committee".
Member of "The Research Committee", Department of Mathematical Sciences, Aalborg University.
Currently Associate Editor of Advances in Applied Probability and Bernoulli. Formerly Associate Editor of the Annals of Applied Probability, and Associate Editor and National Editor of the Scandinavian Journal of Statistics.
Elected member of the International Statistical Institute.
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).
Full picture: click here or here.

Various information:

Recent publications: see below.
Publications: full list.

Collaborators/coauthors.
Courses/teaching.

Scientific meetings.
Talks.

Recent publications:

Comments and corrections to "Statistical Inference and Simulation for Spatial Point Processes": click here.

2010:

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. To appear in Advances in Applied Probability.
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.
J. Møller. Parametric methods. To appear in A Handbook of Spatial Statistics, Eds. A.E. Gelfand, P. Diggle, M. Fuentes, and P. Guttorp, Chapman and Hall/CRC Press.
J. Møller and Carlos Diaz-Avalos. Structured spatio-temporal shot-noise Cox point process models, with a view to modelling forest fires. To appear in Scandinavian Journal of Statistics.
J. Møller and K. Helisova. Likelihood inference for unions of interacting discs. To appear in Scandinavian Journal of Statistics.
J. Møller and Ege Rubak. A model for positively correlated count variables. To appear in International Statistical Review.

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. Submitted for publication.
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. Submitted for publication.
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. Submitted for publication.
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. 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, 627 - 649.
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 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 Ser. B, 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. Bayesian inference for multivariate point processes observed at sparsely distributed times. Journal of Royal Statistical Society Ser. B, 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 using upper and lower 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 via 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 this by clicking 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 Ser. B, 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 Ser. B, 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 and J.G. Rasmussen. A note on a perfect simulation algorithm for marked Hawkes processes. In Spatial point process modelling and its applications. Eds. A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan, Publicacions de la Universitat Jaume I, 187 - 192.
K.K. Berthelsen and J. Møller. A Bayesian MCMC method for point process models with intractable normalising constants. In Spatial point process modelling and its applications (Eds. A. Baddeley, P. Gregori, J. Mateu, R. Stoica and D. Stoyan), Publicacions de la Universitat Jaume I, 7 - 15.
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, 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 Ser. B, 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 Ser. B, 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. Bayesian image analysis with coloured Voronoi tessellations and a view to applications in 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. Submitted.
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. Submitted.
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 Ser. B, 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 Ser. B, 61, 735.
J. Møller and K. Schladitz. Extensions of Fill's algorithm for perfect simulation. Journal of the Royal Statistical Society Ser. B, 61, 955 - 969.

Last modified: Sun Feb 01 14:37:28 Romance Standard Time 2009