Theses

• J. Møller (2000). Aspects of Spatial Statistics, Stochastic Geometry and Markov Chain Monte Carlo, D.Sc. thesis accepted by the Faculty of Engineering and Science, Aalborg University.
• J. Møller (1988). Stochastic Geometry and Markov Models. Ph.D. thesis, Department of Theoretical Statistics, University of Aarhus.
• J. Møller (1984). Normale transformationsmodeller. Statistiske Interna 39, Department of Theoretical Statistics, University of Aarhus. (M.Sc. thesis.)

Monographs

• J. Møller and R.P. Waagepetersen (2004). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC, Boca Raton.
• J. Møller (2003), editor. Spatial Statistics and Computational Methods, Lecture Notes in Statistics 173, Springer-Verlag, New York.
• J. Møller (1994). Lectures on Random Voronoi Tessellations. Lecture Notes in Statistics 87, Springer-Verlag, New York.

Peer-reviewed journal articles

• J. Møller, M. L. Huber and R. L. Wolpert (2010). Perfect simulation and moment properties for the Matérn type III process. Conditionally accepted for publication in Stochastic Processes and teir Applications.
• J. Møller and R.P. Schoenberg (2010). Thinning spatial point processes into Poisson processes. To appear in Advances in Applied Probability.
• B. H. Aukema, J. Zhu, J. Møller, J. G. Rasmussen, and K. F. Raffa. (2010). 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 and Ege Rubak (2010). A model for positively correlated count variables. To appear in International Statistical Review.
• J. Møller and Carlos Diaz-Avalos (2010). 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 (2010). Likelihood inference for unions of interacting discs. To appear in Scandinavian Journal of Statistics.
• J.B. Illian, J. Møller and R.P. Waagepetersen (2009). Hierarchical spatial point process analysis for a plant community with high biodiversity. Environmental and Ecological Statistics, 16, 389 - 405.
• J. Møller and K. Helisova (2008). Power diagrams and interaction processes for unions of discs. Advances in Applied Probability, 40, 321 - 347.
• K.K. Berthelsen and J. Møller (2008). Non-parametric Bayesian inference for inhomogeneous Markov point processes. Australian and New Zealand Journal of Statistics, 50, 257 - 272.
• A. Baddeley, J. Møller and A.G. Pakes (2008). Properties of residuals for spatial point processes. Annals of the Institute of Statistical Mathematics, 60, 627 - 649.
• J. Zhu, J.G. Rasmussen, J. Møller, B.H. Aukema and K.F. Raffa (2008). 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.
• J.G. Rasmussen, J. Møller, B.H. Aukema, K.F. Raffa and J. Zhu (2007). Bayesian inference for multivariate point processes observed at sparsely distributed times. Journal of Royal Statistical Society Ser. B, 69, 701 - 713.
• J. Møller and K. Mengersen (2007). Ergodic averages via dominating processes. Bayesian Analysis, 2, 761 - 782.
• J. Møller and R.P. Waagepetersen (2007). Modern statistics for spatial point processes (with discussion). Scandinavian Journal of Statistics, 34, 643 - 711.
• Ø. Skare, J. Møller and E.B.V. Jensen (2007). Bayesian analysis of spatial point processes in the neighbourhood of Voronoi networks. Statistics and Computing, 17, 369 - 379.
• J. Møller and G.L. Torrisi (2007). Second order analysis for spatial Hawkes processes. Statistics and Probability Letters, 77, 995 - 1003.
• P. McCullagh and J. Møller (2006). The permanental process. Advances in Applied Probability, 38, 873 - 888.
• J. Møller, A.N. Pettitt, K.K. Berthelsen and R.W. Reeves (2006). 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 (2006). Approximate simulation of Hawkes processes. Methodology and Computing in Applied Probability, 8, 53 - 64.
• V. Benes, K. Bodlak, J. Møller and R.P. Waagepetersen (2005). A case study on point process modelling in disease mapping. Image Analysis and Stereology, 24, 159 - 168.
• J. Møller and J.G. Rasmussen (2005). Perfect simulation of Hawkes processes. Advances in Applied Probability, 37, 629 - 646.
• A. Baddeley, R. Turner, J. Møller and M. Hazelton (2005). Residual analysis for spatial point processes (with discussion). Journal of Royal Statistical Society Ser. B, 67, 617 - 666.
• J. Møller and G.L. Torrisi (2005). Generalised shot noise Cox processes. Advances in Applied Probability, 37, 48 - 74.
• J. Møller (2005). Properties of spatial Cox process models. Journal of Statistical Research of Iran, 2, 1-18.
• G. Döge, K. Mecke, J. Møller, D. Stoyan and R.P. Waagepetersen (2004). Grand canonical simulations of hard-disk systems by simulated tempering. International Journal of Modern Physics C, 15, 129 - 147.
• J. Møller (2003). Shot noise Cox processes. Advances in Applied Probability, 35, 614 - 640.
• P.G. Blackwell and J. Møller (2003). Bayesian analysis of deformed tessellation models. Advances in Applied Probability, 35, 4 - 26.
• K.K. Berthelsen and J. Møller (2003). 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.
• K.K. Berthelsen and J. Møller (2002). A primer on perfect simulation for spatial point processes. Bulletin of the Brazilian Mathematical Society, 33, 351 - 367.
• O.F. Christensen, J. Møller and R.P. Waagepetersen (2001). Geometric ergodicity of Metropolis-Hastings algorithms for conditional simulation in generalised linear mixed models. Methodology and Computing in Applied Probability, 3, 309 - 327.
• M.B. Hansen, J. Møller and F.Aa. Tøgersen (2002). 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 (2002). Corrigendum: perfect slice samplers. Journal of Royal Statistical Society Ser. B, 64, 581.
• J. Møller and Ø. Skare (2001). Bayesian image analysis with coloured Voronoi tessellations and a view to applications in reservoir modelling. Statistical Modelling, 1, 213 - 232.
• S. Mase, J. Møller, D. Stoyan, R.P. Waagepetersen and G. Döge (2001). Packing densities and simulated tempering for hard core Gibbs point processes. Annals of the Institute of Statistical Mathematics, 53, 661 - 680.
• A. Brix and J. Møller (2001). Space-time multi type log Gaussian Cox processes with a view to modelling weed data. Scandinavian Journal of Statistics, 28, 471 - 488.
• A. Mira, J. Møller and G.O. Roberts (2001). Perfect slice samplers. Journal of Royal Statistical Society Ser. B, 63, 593 - 606.
• W.S. Kendall and J. Møller (2000). Perfect simulation using dominating processes on ordered spaces, with application to locally stable point processes. Advances in Applied Probability, 32:844-865.
• A. Baddeley, J. Møller and R. Waagepetersen (2000). Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica, 54:329-350.
• J. Møller and K. Schladitz (1999). Extensions of Fill's algorithm for perfect simulation. Journal of the Royal Statistical Society Ser. B, 61:955-969.
• J. Møller (1999). Perfect simulation of conditionally specified models. Journal of Royal Statistical Society Ser. B, 61:251-264.
• O. Häggström, M.N.M. van Lieshout and J. Møller (1999). Characterization results and Markov chain Monte Carlo algorithms including exact simulation for some spatial point processes. Bernoulli, 5:641-659.
• J. Møller, A.R. Syversveen and R.P. Waagepetersen (1998). Log Gaussian Cox processes. Scandinavian Journal of Statistics, 25:451-482.
• J. Møller and R.P. Waagepetersen (1998). Markov connected component fields. Advances in Applied Probability (SGSA), 30:1-35.
• A.J. Baddeley, M.N.M. van Lieshout and J. Møller (1996). Markov properties of cluster processes. Advances in Applied Probability (SGSA), 28:346-355.
• J. Møller and S. Zuyev (1996). Gamma-type results and other related properties of Poisson processes. Advances in Applied Probability (SGSA), 28:662-673.
• H. Högmander and J. Møller (1995). Estimating distribution maps from atlas data using methods of statistical image analysis. Biometrics, 51:393-404.
• J. Møller (1995). Generation of Johnson-Mehl crystals and comparative analysis of models for random nucleation. Advances in Applied Probability (SGSA), 27:367-383.
• C.J. Geyer and J. Møller (1994). Simulation procedures and likelihood inference for spatial point processes. Scandinavian Journal of Statistics, 21:359-373.
• J. Møller and M. Sørensen (1994). Parametric models of spatial birth-and-death processes with a view to modelling linear dune fields. Scandinavian Journal of Statistics, 21:1-19.
• J. Møller (1992). Random Johnson-Mehl tessellations. Advances in Applied Probability, 24:814-844.
• J.L. Jensen and J. Møller (1991). Pseudolikelihood for exponential family models of spatial point processes. Annals of Applied Probability, 3:445-461.
• A. Baddeley and J. Møller (1989). Nearest-neighbour Markov point processes and random sets. International Statistical Review, 2:89-121.
• J. Møller (1989). On the rate of convergence of spatial birth-and-death processes. Annals of the Institute of Statistical Mathematics, 3:565-581.
• J. Møller (1989). Random tessellations in R^d. Advances in Applied Probability, 21:37-73.
• J. Møller (1988). Stereological analysis of particles of varying ellipsoidal shape. Journal of Applied Probability, 25:322-335.
• E.B. Jensen and J. Møller (1986). Stereological versions of integral geometric formulae for n-dimensional ellipsoids. Journal of Applied Probability, 23:1031-1037.
• J. Møller (1986). Bartlett adjustments for structured covariances. Scandinavian Journal of Statistics, 13:1-15.

Peer-reviewed book chapters

• J. Møller (2010). Parametric methods. To appear in A Handbook of Spatial Statistics, edited by A.E. Gelfand, P. Diggle, M. Fuentes, and P. Guttorp, to be published by Chapmand and Hall/CRC Press.
• J. Møller (2010). 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 D. Stoyan (2010). 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 and K. Mengersen (2007). 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.
• K.K. Berthelsen and J. Møller (2006). 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.
• J. Møller (2003). 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 and R.P. Waagepetersen (2003). An introduction to simulation based inference for spatial point processes. In Spatial Statistics and Computational Methods, Ed. J. Møller, Lecture Notes in Statistics, 173, Springer-Verlag, 143 - 198.
• K.K. Berthelsen and J. Møller (2002). Spatial jump processes and perfect simulation. In Morphology of Condensed Matter, Eds. K. Mecke and D. Stoyan, Lecture Notes in Physics, Vol. 600, Springer-Verlag, 391 - 417.
• J. Møller and R.P. Waagepetersen (2002). Statistical inference for Cox processes. Spatial Cluster Modelling, Eds. Andrew B. Lawson and David Denison, Chapman and Hall/CRC, 37 - 60.
• J. Møller (2001). 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.
• J. Møller (1999). 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 (1999). 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 (1998). A review on probabilistic models and results for Voronoi tessellations. In Voronoi's Impact on Modern Science, Eds. P. Engel and H. Syta, Institute of Mathematics of the National Academy of Sciences of Ukraine, Kyiv, 254-265.

Peer-reviewed conference proceedings

• J. Møller and K. Helisova (2009). 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 (2009). 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 (2009). 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 J.G. Rasmussen (2009). 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. Møller and J.G. Rasmussen (2004). 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 (2004). 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.
• V. Benes, K. Bodlak, J. Møller and R. Waagepetersen (2003). Application of log Gaussian Cox processes in disease mapping. Proceedings of the ISI Conference on Environmental Statistics and Health, Santiago de Compostela. Eds. J. Mateu, D. Holland and W. Gonzalez-Manetiga, 95 - 105.
• A. Mira, J. Møller and G. O. Roberts (2001). Perfect simple slice samplers. In Bulletin of the International Statistical Institute, 53rd Session Proceedings, Tome LIX, Book 1. 73 - 79.
• J. Møller, A.R. Syversveen and R. Waagepetersen (1997). Log Gaussian Cox processes: A statistical model for analyzing stand structural heterogeneity in forestry. In Proc. First European Conference for Information Technology in Agriculture, Eds. H. Kure, I. Thysen and A.R. Kristensen, Department of Mathematics and Physics, The Royal Veterinary and Agricultural University, Denmark.

Discussion contributions

• J. Møller (2008). Contribution to the discussion of P. McCullagh (2008): Sampling bias and logistic models. Journal of Royal Statistical Society Ser. B, 70, 669.
• J. Møller (2006). 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.
• J. Møller (2003). Contribution to the discussion of S.P. Brooks, P. Giudici and G.O. Roberts (2003): Efficient constructions of reversible jump Markov chain Monte Carlo proposal distributions. Journal of Royal Statistical Society Series B, 65, 42 - 43.
• J. Møller and R.P. Waagepetersen (1999). Contribution to the discussion of J. Besag and D. Higdon (1999): Bayesian analysis of agricultural field experiments. Journal of the Royal Statistical Society Series B, 61, 735.
• J. Møller (1994). Contribution to the discussion of N.L. Hjort and H. Omre (1994): Topics in spatial statistics. Scandinavian Journal of Statistics, 21:346-349.
• J. Møller (1993). Contribution to the discussion on the meeting on the Gibbs Sampler and other Markov Chain Monte Carlo methods. Journal of the Royal Statistical Society Series B, 55:84-85.
• J. Møller (1992). Contribution to the discussion of C.J. Geyer and E.A. Thompson (1992): Constrained Monte Carlo maximum likelihood for dependent data. Journal of the Royal Statistical Society Series B, 54:692-693.

Technical reports submitted to peer-reviewed journals, etc.

• J. Møller and M.L. Yiu (2009). The distribution of communication cost for a mobile service scenario. Research Report R-2009-11, Department of Mathematical Sciences, Aalborg University.

Technical reports

• J. Møller and G. Nicholls (1999). Perfect simulation for sample-based inference. Research Report R-99-2011, Department of Mathematical Sciences, Aalborg University. Conditionally accepted for Statistics and Computing.
• W.S. Kendall and J. Møller (1999). 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 (1999). Perfect implementation of a Metropolis-Hastings simulation of Markov point processes. Research Report 348, Department of Statistics, University of Warwick.
• J. Møller (1992). Extensions of the Swendsen-Wang algorithm for simulating spatial point processes. Research Report 246, Department of Theoretical Statistics, University of Aarhus.
• J. Møller, E.B. Jensen, J.S. Petersen and H.J.G. Gundersen (1989). Modelling an aggregate of space-filling cells from sectional data. Research Report 182, Department of Theoretical Statistics, University of Aarhus.
• J. Møller (1985). A simple derivation of a formula of Blaschke and Petkantschin. Research Report 138, Department of Theoretical Statistics, University of Aarhus.
• J. Møller (1985). On the accuracy of the $\chi$-squared approximation of the Bartlett adjusted log likelihood ratio statistic for three testing problems. Research Report 129, Department of Theoretical Statistics, University of Aarhus.

Lecture notes

• K.K. Berthelsen and J. Møller (2004). 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.
• J. Møller og R.P. Waagepetersen (2003). Lidt om kurver og geometrisk kontinuitet. Institut for Matematiske Fag, Aalborg Universitet.
• J. Møller (1999). Notes on Markov chain Monte Carlo methods. Department of Mathematical Sciences, Aalborg University.
• J. Møller (1995). Centrale Statistiske Modeller og Likelihood Baserede Metoder. Institute of Mathematical Sciences, University of Aarhus. (285 pages.)
• J. Møller (1990). Lectures on Markov random fields. Department of Theoretical Statistics, University of Aarhus.
• J. Møller (1989). Stokastiske Tessellationer. Department of Theoretical Statistics, University of Aarhus.