Publications

All data comes from articles registered in PURE.

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Baudoin, F. & O'Connell, N. (2011). Exponential functionals of Brownian motion and class-one Whittaker functions. Annales de l'institut Henri Poincare (B) Probability and Statistics, 47(4), 1096-1120. https://doi.org/10.1214/10-AIHP401

Asmussen, S., Jensen, J. L. & Rojas-Nandayapa, L. (2014). Exponential Family Techniques for the Lognormal Left Tail. T.N. Thiele Centre, Department of Mathematics, Aarhus University. Thiele Research Reports No. 01 http://math.au.dk/publs?publid=1010

Asmussen, S., Jensen, J. L. & Rojas-Nandayapa, L. (2016). Exponential Family Techniques for the Lognormal Left Tail. Scandinavian Journal of Statistics, 43(3), 774-787. https://doi.org/10.1111/sjos.12203

Asmussen, S. (1989). Exponential families generated by phase-type distributions and other Markov lifetimes. Scandinavian Journal of Statistics, 16(4), 319-334.

Jørgensen, B. & Labouriau, R. S. (1992). Exponential Families and Theoretical Inference. Springer. http://www.zentralblatt-math.org/zmath/en/search/?q=an:0968.62500&format=complete

Jørgensen, B. & Labouriau, R. S. (2012). Exponential Families and Theoretical Inference. (2 ed.) Springer.

Asmussen, S. (1990). Exponential families and regression in the Monte Carlo study of queues and random walks. Annals of Statistics, 18(4), 1851-1867.

Jørgensen, B. & Labouriau, R. (2000). Expoenetial Families and theoretical Inference.

Agergaard, T. E., Nielsen, K. H., Labouriau, R. & Fage-Butler, A. (2026). Exploring Trust and Literacy in Engagement with Generative AI and Science Information Behavior. Media and Communication, 14, Article 11357. https://doi.org/10.17645/mac.11357

Averhoff, M. & Thøgersen, J. (2025). Experience rating in the Cramér-Lundberg model. Insurance: Mathematics and Economics, 124, Article 103128. https://doi.org/10.1016/j.insmatheco.2025.103128

Rønn-Nielsen, A. & Jensen, E. B. V. (2016). Excursion sets of infinitely divisible random fields with convolution equivalent Lévy measure. Centre for Stochastic Geometry and advanced Bioimaging, Aarhus University. CSGB Research Reports Vol. 2016/11 http://math.au.dk/publs?publid=1075

Rønn-Nielsen, A. & Jensen, E. B. V. (2017). Excursion sets of infinitely divisible random fields with convolution equivalent Lévy measure. Journal of Applied Probability, 54(3), 833-851. https://doi.org/10.1017/jpr.2017.37

Asmussen, S. & Jobmann, M. (2002). Exact buffer overflow calculations for queues via martingales. Queueing Syst., 42(1), 63-90.

Asmussen, S. & Collamore, J. F. (1999). Exact asymptotics for a large deviations problem for the GI/G/1 queue. Markov Process. Related Fields, 5(4), 451-476.

Strauch, C. (2016). Exact adaptive pointwise drift estimation for multidimensional ergodic diffusions. Prob. Theory Rel. Fields, 164(1-2), 361-400.

Thirstrup, J. P., Guldbrandtsen, B., Labouriau, R., Anistoroae, R. M., Christensen, K., Fredholm, M. & Nielsen, V. H. (2012). Evidence for similar location of QTL for guard hair length and thickness in mink (Neovison vison). Poster session presented at 33rd International Society of Animal Genetics, Cairns, Australia.

Thirstrup, J. P., Guldbrandtsen, B., Labouriau, R., Anistoroaei, R. M., Kristensen, K., Fredholm, M. & Nielsen, V. H. (2012). Evidence for similar location of QTL for guard hair length and thicknes in mink (Neovison vison). In Programme and Abstract Book (pp. 115-116).

Borup, K. & Andersen, L. N. (2021). Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation. Paper presented at Conference on Neural Information Processing Systems 2021. https://proceedings.neurips.cc/paper/2021/file/2adcefe38fbcd3dcd45908fbab1bf628-Paper.pdf

Borup, K. & Andersen, L. N. (2021). Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation. In MA. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang & J. Wortman Vaughan (Eds.), Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021 (pp. 5316-5327). Neural Information Processing Systems Foundation. https://proceedings.neurips.cc/paper_files/paper/2021/file/2adcefe38fbcd3dcd45908fbab1bf628-Paper.pdf

Pedersen, J., Hafner, S. D., Pacholski, A. S., I. Karlsson, V., Rong, L., Labouriau, R. & Kamp, J. N. (2024). Evaluation of optimized flux chamber design for measurement of ammonia emission after field application of slurry with full-scale farm machinery. Atmospheric Measurement Techniques, 17(14), 4493–4505. https://doi.org/10.5194/amt-17-4493-2024

Baudoin, F. & Coutin, L. (2005). Étude en temps petit des solutions d'EDS conduites par des mouvements browniens fractionnaires. Comptes Rendus Mathematique, 341(1), 39-42. https://doi.org/10.1016/j.crma.2005.05.010

Asmussen, S. (1986). Estimation theory for multitype branching processes. In J. Janssen (Ed.), Semi-Markov models: theory and applications (pp. 385-395). Plenum Publishing Corporation.

Podolskij, M. & Vetter, M. (2009). Estimation of volatility functionals in the simultaneous presence of microstructure noise and jumps. Bernoulli, 15(3), 634-658. https://doi.org/10.3150/08-BEJ167

Sevón-Aimonen, M.-L., Labouriau, R., Maia, R. P., Haltia, S. & Uimari, P. (2013). Estimation of variance components of sow longevity traits using discrete time model. In Book of Abstracts of the 64th Annual Meeting of the European Association for Animal Production (2013 ed., Vol. 19, pp. 390). Wageningen Academic Publishers. http://www.eaap.org/Previous_Annual_Meetings/2013Nantes/Nantes_2013_Abstracts.pdf

Kiderlen, M. (2008). Estimation of the mean normal measure from flat sections. Advances in Applied Probability, 40(1), 31-48. https://doi.org/10.1239/aap/1208358885

Mazur, S., Otryakhin, D. & Podolskij, M. (2020). Estimation of the linear fractional stable motion. Bernoulli, 26(1), 226-252. https://doi.org/10.3150/19-BEJ1124

Podolskij, M. & Lebovits, J. (2017). Estimation of the global regularity of a multifractional Brownian motion. Electronic Journal of Statistics, 11(1), 78-98. https://doi.org/10.1214/16-EJS1221

Lebovits, J. & Podolskij, M. (2016). Estimation of the global regularity of a multifractional Brownian motion. Institut for Økonomi, Aarhus Universitet. CREATES Research Paper No. 2016-33

Kiderlen, M. & Jensen, E. B. V. (2003). Estimation of the directional measure of planar random sets by digitization. Advances in Applied Probability, 35(3), 583-602.

Ziegel, J. & Kiderlen, M. (2010). Estimation of surface area and surface area measure of three-dimensional sets from digitizations. Image and Vision Computing, 28(1), 64-77. https://doi.org/10.1016/j.imavis.2009.04.013

Jensen, J. L. & Sørensen, M. (1986). Estimation of some aeolian saltation transport parameters: a re- analysis of Williams' data. Sedimentology, 33(4), 547-558. https://doi.org/10.1111/j.1365-3091.1986.tb00760.x

Rønn-Nielsen, A., Sporring, J. & Jensen, E. B. V. (2016). Estimation of sample spacing in stochastic processes. Centre for Stochastic Geometry and advanced Bioimaging, Aarhus University. CSGB Research Reports Vol. 17, 2016 http://math.au.dk/forskning/publikationer/instituttets-serier/publication/publid/1091/

Rønn-Nielsen, A., Sporring, J. & Jensen, E. B. V. (2017). Estimation of sample spacing in stochastic processes. Image Analysis and Stereology, 36(1), 43-49. https://doi.org/10.5566/ias.1681

Martínez, I., Hahn, U., Nygaard, J. V. & Londono, C. S. (2026). Estimation of Pores Via Artificial Pore Intersections. Image Analysis and Stereology, 45, 167-184. Advance online publication. https://doi.org/10.5566/ias.3970

Theil, P. K., Nielsen, T. T., Kristensen, N. B., Labouriau, R., Danielsen, V., Lauridsen, C. & Jakobsen, K. (2002). Estimation of milk production in lactating sows by determination of deuterated water turnover in three piglets per litter. Acta Agriculturae Scandinavica, Section A - Animal Science, 52, 221-232.

Hahn, U. (1998). Estimation of local density in gradient surface and fibre processes. Advances in Applied Probability, 30, 279-280.

Dette, H., Podolskij, M. & Vetter, M. (2006). Estimation of integrated volatility in continuous-time financial models with applications to goodness-of-fit testing. Scandinavian Journal of Statistics, 33(2), 259-278. https://doi.org/10.1111/j.1467-9469.2006.00479.x

Neumann, M., Hirsch, C., Staněk, J., Beneš, V. & Schmidt, V. (2019). Estimation of geodesic tortuosity and constrictivity in stationary random closed sets. Scandinavian Journal of Statistics, 46(3), 848-884. https://doi.org/10.1111/sjos.12375

Kärkkäinen, S. & Jensen, E. B. V. (2001). Estimation of fibre orientation from digital images. Image Analysis and Stereology, 20(3), 199-202. https://doi.org/10.5566/ias.v20.p199-202

Overgaard, M., Parner, E. T. & Pedersen, J. (2018). Estimating the variance in a pseudo-observation scheme with competing risks. Scandinavian Journal of Statistics, 45(4), 923-940. https://doi.org/10.1111/sjos.12328

Thórisdóttir, Ó., H.Rafati, A. & Kiderlen, M. (2014). Estimating the surface area of nonconvex particles from central planar sections. Journal of Microscopy, 255(1), 49–64. https://doi.org/10.1111/jmi.12136

Thórisdóttir, Ó., H.Rafati, A. & Kiderlen, M. (2013). Estimating the surface area of non-convex particles from central planar sections. Centre for Stochastic Geometry and advanced Bioimaging, Aarhus University. CSGB Research Reports No. 08 http://math.au.dk/publs?publid=987

Kiderlen, M. (2006). Estimating the Euler Characteristic of a planar set from a digital image. Journal of Visual Communication and Image Representation, 17, 1237-1255.

Dexheimer, N. & Strauch, C. (2022). Estimating the characteristics of stochastic damping Hamiltonian systems from continuous observations. Stochastic Processes and Their Applications, 153, 321-362. https://doi.org/10.1016/j.spa.2022.08.008

Jensen, J. L., Raarup, M. K. & Rubak, E. (2012). Estimating protein-protein interaction affinity in single living cells using Förster resonance energy transfer measurements. Centre for Stochastic Geometry and advanced Bioimaging, Aarhus University. CSGB Research Reports No. 12

Ziegel, J. F., Nyengaard, J. R. & Vedel Jensen, E. B. (2015). Estimating Particle Shape and Orientation Using Volume Tensors. Scandinavian Journal of Statistics, 42(3), 813-831. https://doi.org/10.1111/sjos.12138

Davtian, A., Hahn, U., Ohser, J. & Stoyan, D. (2000). Estimating number density $N_V$ - a comparison of an improved Saltykov estimator and the disector method. Image Analysis and Stereology, 19, 209-214.

Hahn, U. & Stoyan, D. (1997). Estimating local surface area density in gradient materials. In Proceedings of International Conference on the Quantitative Description of Materials Microstructure - QMAT '97. Warsaw, 16-19 April 1997 (pp. 19-26). Jagiellonian University Press.

Charmouh, A. P., Porsborg, P. S., Hansen, L. T., Besenbacher, S., Boeg Winge, S., Almstrup, K., Hobolth, A., Bataillon, T. & Schierup, M. H. (2025). Estimating Gene Conversion Tract Length and Rate From PacBio HiFi Data. Molecular Biology and Evolution, 42(2), Article msaf019. https://doi.org/10.1093/molbev/msaf019

Labouriau, R. (1999). Estimating functions for semiparametric models with moment restrictions. Technical Report of the Biometry Research Unit.

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Revised 10.06.2026

Lars Madsen