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Department of Mathematics

Publications

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Ljungdahl, M. M. & Podolskij, M. (2022). Multidimensional parameter estimation of heavy-tailed moving averages. Scandinavian Journal of Statistics, 49(2), 593-624. https://doi.org/10.1111/sjos.12527

Nielsen, M. M., Tataru, P., Madsen, T., Hobolth, A. & Pedersen, J. S. (2016). Motif discovery in ranked lists of sequences. (pp. 1-4). http://biorxiv.org/content/biorxiv/suppl/2016/01/11/035956.DC2/035956-1.pdf

Mrkvička, T., Soubeyrand, S., Myllymäki, M., Grabarnik, P. & Hahn, U. (2016). Monte Carlo testing in spatial statistics, with applications to spatial residuals. Spatial Statistics, 18(A), 40-53. https://doi.org/10.1016/j.spasta.2016.04.005

Asmussen, S. & Sigman, K. (1996). Monotone stochastic recursions and their duals. Probab. Engrg. Inform. Sci., 10(1), 1-20.

Dörnemann, N. T. & Kutta, T. (2025). Monitoring Time Series with Short Detection Delay. Electronic Journal of Statistics, 19(1), 2239-2275. https://doi.org/10.1214/25-EJS2385

Dörnemann, N., Kokoszka, P., Kutta, T. & Lee, S. (2025). Monitoring for a Phase Transition in a Time Series of Wigner Matrices.

Asmussen, S. & Bladt, M. (2022). Moments and polynomial expansions in discrete matrix-analytic models. Stochastic Processes and Their Applications, 150, 1165-1188. https://doi.org/10.1016/j.spa.2021.12.002

Baudoin, F., Chen, H. & Ouyang, C. (2024). Moment estimates for the stochastic heat equation on Cartan-Hadamard manifolds.

Baudoin, F., Chen, H. & Ouyang, C. (2025). Moment estimates for the stochastic heat equation on Cartan-Hadamard manifolds. Journal of Mathematical Analysis and Applications, 552(2), Article 129805. https://doi.org/10.1016/j.jmaa.2025.129805

Dalgaard, T., Vitved, L., Skjoedt, K., Thomsen, B., Labouriau, R., Jensen, K. H. & Juul-Madsen, H. R. (2004). Molecular characterization of MHC class I (B-F) mRNA variants from chickens differing in resistance to Marek's disease: Gene bank accession no. AY821520. Dataset

Dalgaard, T. S., Vitved, L., Skjødt, K., Thomsen, B., Labouriau, R., Jensen, K. H. & Juul-Madsen, H. R. (2005). Molecular characterization of Major Histocompatibility Complex class I (B-F) mRNA variants from chickens differing in resistance to Marek's disease. Scandinavian Journal of Immunology, 62, 259-270. https://doi.org/10.1111/j.1365-3083.2005.01652.x

Spitzer, K., Pelizzola, M. & Futschik, A. (2020). Modifying the chi-square and the cmh test for population genetic inference: Adapting to overdispersion. Annals of Applied Statistics, 14(1), 202-220. https://doi.org/10.1214/19-AOAS1301

Heerten, N., Hirsch, C. & Otto, M. (2025). Moderate deviations for weight-dependent random connection models. Journal of Applied Probability, 62(4), 1406-1428. https://doi.org/10.1017/jpr.2025.13

Hirsch, C., Neuhäuser, D. & Schmidt, V. (2016). Moderate deviations for shortest-path lengths on random segment processes. ESAIM - Probability and Statistics, 20, 261-292.

Pelizzola, M., Laursen, R. & Hobolth, A. (2023). Model selection and robust inference of mutational signatures using Negative Binomial non-negative matrix factorization. BMC Bioinformatics, 24(1), Article 187. https://doi.org/10.1186/s12859-023-05304-1

Hobolth, A. & Jensen, E. B. V. (2000). Modelling stochastic chances in curve shape, with an application to cancer diagnostics. Advances in Applied Probability, 32(2), 344-362. https://doi.org/10.1239/aap/1013540167

Thorarinsdottir, T. L. & Jensen, E. B. V. (2006). Modelling resting state networks in the human brain. In R. Lechnerová, I. Saxl & V. Benes (Eds.), Proceedings S⁴G, International Conference on Stereology, Spatial Statistics and Stochastic Geometry (pp. 137-147). Union Czech Mathematicians and Physicists.

Labouriau, R. & Madsen, P. (2009). Modelling censored discrete time data with a view towards applications in animal quantitative genetics. EAAP Book of Abstracts, 604. http://www.wageningenacademic.com/_clientfiles/EAAP/eaap2009-e.pdf

Labouriau, R. & Madsen, P. (2009). Modelling censored discrete time data with a view towards applications in animal quantitative genetics. Poster session presented at Annual Meeting of of the European Association for Animal Production, EAAP, Barcelona, Spain.

Asmussen, S. (2013). Modeling and performance of Bonus-Malus Systems: Stationarity versus age-correction. T.N. Thiele Centre, Department of Mathematics, Aarhus University. Thiele Research Reports No. 05 http://math.au.dk/publs?publid=989

Labouriau, R. (2006). Mixture models for survival analysis with a view towards animal genetics applications. In Book of abstracts: CD communication 26-08, 4 pp (pp. 241). WCGALP. http://www.wcgalp8.org.br/wcgalp8/articles/paper/26_717-1039.pdf

Andersen, K., Louveaux, Q. & Weismantel, R. (2010). Mixed-integer sets from two rows of two adjacent simplex bases. Mathematical Programming, 124/1-2, 455-480. https://doi.org/10.1007/s10107-010-0376-4

Andersen, K. & Weismantel, R. (2008). Mixed integer linear sets. SIAG/OPT Views-and-News, 19(2), 8-14.

Pedersen, J., Romio, C., Labouriau, R., Pacholski, A. S. & Hafner, S. D. (2023). Mitigating ammonia emission from slurry digestate applied to winter wheat crops: effect of digestate composition, digestate treatment, and application technique. Abstract from RAMIRAN, Cambridge, United Kingdom.

Hansen, M. B., Jensen, J. L. & Lauritzen, S. L. (Eds.) (2001). Mini-proceedings: Second MaPhySto Workshop on Inverse Problems: Inverse Problems from a Statistical Perspective. MaPhySto, Aarhus University.

Guillot, J.-C., Jensen, J. L., Jensen, A. & Holm-Jacobsen, B. (Eds.) (1999). Mini-proceedings: First MaPhySto Workshop on Inverse Problems: Inverse Problems in Stratified Media. MaPhySto, Aarhus University.

Theil, P. K., Kristensen, N. B., Jørgensen, H., Labouriau, R. & Jakobsen, K. (2007). Milk intake and carbon dioxide production of piglets determined with the doubly labelled water technique. Animal, 1, 881-888. https://doi.org/10.1017/S1751731107000031

Jacod, J., Li, Y., Mykland, P. A., Podolskij, M. & Vetter, M. (2009). Microstructure noise in the continuous case: The pre-averaging approach. Stochastic Processes and Their Applications, 119(7), 2249-2276. https://doi.org/10.1016/j.spa.2008.11.004

Duan, Y.-F., Kong, X., Schramm, A., Labouriau, R., Eriksen, J. & Petersen, S. O. (2017). Microbial N Transformations and N2O Emission after Simulated Grassland Cultivation: Effects of the Nitrification Inhibitor 3,4-Dimethylpyrazole Phosphate (DMPP). Applied and Environmental Microbiology, 83(1), Article e02019-16. https://doi.org/10.1128/AEM.02019-16

Schou, T. W., Labouriau, R., Permin, A., Christensen, J. P., Sørensen, P., Cu, H. P., Nguyen, V. K. & Juul-Madsen, H. R. (2010). MHC haplotype and susceptibility to experimental infections (Salmonella Enteritidis, Pasteurella multocida orAscaridia galli) in a commercial and an indigenous chicken breed. Veterinary Immunology and Immunopathology, 135(1-2), 52-63. https://doi.org/10.1016/j.vetimm.2009.10.030

Kandel, T. P., Karki, S., Elsgaard, L., Labouriau, R. & Lærke, P. E. (2020). Methane fluxes from a rewetted agricultural fen during two initial years of paludiculture. Science of the Total Environment, 713, Article 136670. https://doi.org/10.1016/j.scitotenv.2020.136670

Barndorff-Nielsen, O. & Pedersen, J. (2010). Meta-Times and Extended Subordination. Thiele Centre, Institut for Matematiske Fag, Aarhus Universitet.

Barndorff-Nielsen, O. E. & Pedersen, J. (2012). Meta-times and extended subordination. Theory of Probability and Its Applications, 56(2), 319-327. https://doi.org/10.1137/S0040585X97985467

Theil, P. K., Flummer, C., Hurley, W. L., Kristensen, N. B., Labouriau, R. & Sørensen, M. T. (2014). Mechanistic model to predict colostrum intake based on deuterium oxide dilution technique data and impact of gestation and prefarrowing diets on piglet intake and sow yield of colostrum. Journal of Animal Science, 92(12), 5507-5519. https://doi.org/10.2527/jas.2014-7841

Eriksen, R. K. & Kiderlen, M. (2023). Mean Surface and Volume Particle Tensors under Restricted L-isotropy and Associated Ellipsoids. Advances in Geometry, 23(2).

Jensen, J. L. (1988). Maximum-likelihood estimation of the hyperbolic parameters from grouped observations. Computers & Geosciences, 14(3), 389-408. https://doi.org/10.1016/0098-3004(88)90069-6

Hobolth, A. & Wiuf, C. (2024). Maximum likelihood estimation and natural pairwise estimating equations are identical for three sequences and a symmetric 2-state substitution model. Theoretical Population Biology, 156, 1-4. https://doi.org/10.1016/j.tpb.2023.12.004

Jensen, J. L. (2006). Maximum likelihood classifiers in microarray studies. Institut for Matematiske Fag, Aarhus Universitet.

Hirsch, C., Bharti, A., Pedersen, T. & Waagepetersen, R. (2021). Maximum Likelihood Calibration of Stochastic Multipath Radio Channel Models. IEEE Transactions on Antennas and Propagation, 69(7), 4058-4069. Article 9298915. https://doi.org/10.1109/TAP.2020.3044379

Asmussen, S. & Bladt, M. (2025). Matrix representations of Wiener-Hopf factorisations for Lévy processes. Journal of Applied Probability, 62(3), 1028-1043. https://doi.org/10.1017/jpr.2024.112

Bladt, M., Asmussen, S. & Steffensen, M. (2020). Matrix representations of life insurance payments. European Actuarial Journal, 10(1), 29-67. https://doi.org/10.1007/s13385-019-00222-0

Asmussen, S. (2000). Matrix-analytic models and their analysis. Scandinavian Journal of Statistics, 27(2), 193-226.

Andersen, I. T. & Hahn, U. (2015). Matérn thinned Cox processes. Centre for Stochastic Geometry and advanced Bioimaging, Aarhus University. CSGB Research Reports No. 08

Andersen, I. T. & Hahn, U. (2016). Matérn thinned Cox processes. Spatial Statistics, 15, 1-21. https://doi.org/10.1016/j.spasta.2015.10.005

Kiderlen, M. & Hörig, M. (2013). Matérn's hard core models of types I and II with arbitrary compact grains. Centre for Stochastic Geometry and advanced Bioimaging, Aarhus University. CSGB Research Reports No. 05 http://math.au.dk/publs?publid=980

Basse-O'Connor, A., Graversen, S.-E. & Pedersen, J. (2010). Martingale-type processes indexed by the real line. Alea, 7, 117-137. http://alea.impa.br/articles/v7/07-06.pdf

Bañuelos, R. & Baudoin, F. (2013). Martingale Transforms and Their Projection Operators on Manifolds. Potential Analysis, 38(4), 1071-1089. https://doi.org/10.1007/s11118-012-9307-8

Asmussen, S. & Keiding, N. (1978). Martingale central limit theorems and asymptotic estimation theory for multitype branching processes. Advances in Applied Probability, 10(1), 109-129.

Asmussen, S., Lipsky, L. & Thompson, S. (2015). Markov Renewal Methods in Restart Problems in Complex Systems. T.N. Thiele Centre, Department of Mathematics, Aarhus University. Thiele Research Reports No. 01 http://math.au.dk/publs?publid=1034

Asmussen, S., Lipsky, L. & Thompson, S. (2015). Markov Renewal Methods in Restart Problems in Complex Systems. In M. Podolskij, R. Stelzer, S. Thorbjørnsen & D. A. E. Veraart (Eds.), The Fascination of Probability, Statistics and their Applications: In Honour of Ole E. Barndorff-Nielsen (pp. 501-527). Springer. https://doi.org/10.1007/978-3-319-25826-3_23

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Lars Madsen