Institut for Matematik

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Hirsch, C. P., Otto, M. & Svane, A. M. (2024). NORMAL APPROXIMATION FOR GIBBS PROCESSES VIA DISAGREEMENT COUPLINGS. https://doi.org/10.1214/25-EJP1351
Dorph-Petersen, K. A., Gundersen, H. J. & Jensen, E. B. V. (2000). Non-uniform systematic sampling in stereology. Journal of Microscopy, 200(Pt 2), 148-57. https://doi.org/10.1046/j.1365-2818.2000.00750.x
Dorph-Petersen, K.-A., Gundersen, H. J. G., Jensen, E. B. V. & Kiêu, K. (2001). Non-uniform systematic sampling for area estimation of planar objects. I C. Babu Rao, P. Kalyanasundaram, KK. Ray & B. Raj (red.), Image Analysis in Materials and Life Sciences (s. 4-8)
Strauch, C. & Tiepner, A. (2024). Nonparametric velocity estimation in stochastic convection-diffusion equations from multiple local measurements. https://doi.org/10.48550/arXiv.2402.08353
Christensen, S. & Strauch, C. (2019). Nonparametric learning for impulse control problems. https://arxiv.org/abs/1909.09528
Waltoft, B. L. & Hobolth, A. (2018). Non-parametric estimation of population size changes from the site frequency spectrum. Statistical Applications in Genetics and Molecular Biology, 17(3), Artikel 20170061. https://doi.org/10.1515/sagmb-2017-0061
Juul-Kristensen, T., Thiesen, C., Haurum, L. W., Keller-Socin, J. G., Lendamba, R. W., Zoleko Manego, R., Betouke, M. E., Knudsen, B. R., Pareja, E., Pareja-Tobes, E., Labouriau, R., Ghyslain, M.-N. & Tesauro, C. (2024). Non-Invasive Malaria Detection in Sub-Saharan Africa Using a DNA-Based Sensor System. Sensors, 24(24), Artikel 7947. https://doi.org/10.3390/s24247947
Rasmussen, M. J., Bertl, J., Guo, Q., Nielsen, M. M., Świtnicki, M., Hornshøj, H., Madsen, T., Hobolth, A. & Pedersen, J. S. (2017). Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate. eLife, 6, Artikel e21778. https://doi.org/10.7554/eLife.21778
Molnár, K., Stephensen, H. J. T., Hahn, U., Xu, Z., Hasselholt, S. & Nyengaard, J. R. (2022). Node of Ranvier changes in a new mouse model of amyotrophic lateral sclerosis (ALS). Abstract fra 5th Annual Research Meeting at the Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Danmark.
Taghizadeh-Toosi, A., Janz, B., Labouriau, R., Olesen, J. E., Butterbach-Bahl, K. & Petersen, S. O. (2021). Nitrous oxide emissions from red clover and winter wheat residues depend on interacting effects of distribution, soil N availability and moisture level. Plant and Soil, 466(1-2), 121-138. https://doi.org/10.1007/s11104-021-05030-8
Baral, K. R., Labouriau, R., Olesen, J. E. & Petersen, S. O. (2017). Nitrous oxide emissions and nitrogen use efficiency of manure and digestates applied to spring barley. Agriculture, Ecosystems & Environment, 239, 188-198. https://doi.org/10.1016/j.agee.2017.01.012
Fontaine, D., Feng, L., Labouriau, R., Møller, H. B., Eriksen, J. & Sørensen, P. (2020). Nitrogen and Sulfur Availability in Digestates from Anaerobic Co-digestion of Cover Crops, Straw and Cattle Manure. Journal of Soil Science and Plant Nutrition, 20(2), 621-636. https://doi.org/10.1007/s42729-019-00151-7
Vogeler, I., Hansen, E. M., Nielsen, S., Labouriau, R., Cichota, R., Olesen, J. E. & Thomsen, I. K. (2020). Nitrate leaching from suction cup data: Influence of method of drainage calculation and concentration interpolation. Journal of Environmental Quality, 49(2), 440-449. https://doi.org/10.1002/jeq2.20020
Podolskij, M. & Ziggel, D. (2010). New tests for jumps in semimartingale models. Statistical Inference for Stochastic Processes, 13(1), 15-41. https://doi.org/10.1007/s11203-009-9037-8
Barndorff-Nielsen, O. E., Jensen, J. L. & Kendall, W. S. (red.) (1993). Networks and chaos: statistical and probabilistic aspects. Monographs on statistics and applied probability Bind 50
Asmussen, S. & Kurtz, T. G. (1980). Necessary and sufficient conditions for complete convergence in the law of large numbers. Annals of Probability, 8(1), 176-182.
Juul, M., Madsen, T., Guo, Q., Bertl, J., Hobolth, A., Kellis, M. & Pedersen, J. S. (2019). ncdDetect2: Improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation. Bioinformatics, 35(2), 189-199. https://doi.org/10.1093/bioinformatics/bty511
Asmussen, S., Christensen, B. J. & Thøgersen, J. (2019). Nash equilibrium premium strategies for push–pull competition in a frictional non-life insurance market. Insurance: Mathematics and Economics, 87, 92-100. https://doi.org/10.1016/j.insmatheco.2019.02.002
Pimentel Maia, R., Madsen, P. & Labouriau, R. (2014). Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits. Journal of Applied Statistics, 41(6), 1286-1306. https://doi.org/10.1080/02664763.2013.868416
Pimentel Maia, R., Madsen, P. & Labouriau, R. (2013). Multivariate Survival Mixed Models for Genetic Analysis of Longevity Traits. (s. 1-36). ArXiv. http://arxiv.org/abs/1303.0810
Barndorff-Nielsen, O. E., Pedersen, J. & Sato, K.-I. (2001). Multivariate subordination, self-decomposability and stability. Advances in Applied Probability, 33(1), 160-187.
Basse-O'Connor, A., Nielsen, M. S., Pedersen, J. & Rohde, V. U. (2019). Multivariate stochastic delay differential equations and CAR representations of CARMA processes. Stochastic Processes and Their Applications, 129(10), 4119-4143. https://doi.org/10.1016/j.spa.2018.11.011
Hobolth, A., Bladt, M. & Andersen, L. N. (2021). Multivariate phase-type theory for the site frequency spectrum. Journal of Mathematical Biology, 83(6-7), Artikel 63. https://doi.org/10.1007/s00285-021-01689-w
Pelck, J. S., Holthusen, H., Edelenbos, M., Luca, A. & Labouriau, R. (2021). Multivariate Methods for Detection of Rubbery Rot in Storage Apples by Monitoring Volatile Organic Compounds: An Example of Multivariate Generalised Mixed Models.
Labouriau, R. (2016). Multivariate Generalized Linear Mixed Models with High Complexity / Modèles linéaires généralisés mixtes multivariés avec complexité élevée. Abstract fra 44th Annual Meeting of the Statistical Society of Canada, Canada.
Pelck, J. S. & Labouriau, R. (2021). Multivariate Generalised Linear Mixed Models With Graphical Latent Covariance Structure. arXiv:stat.ME.
Blæsild, P. & Jensen, J. L. (1981). Multivariate distributions of hyperbolic type. I Statistical distributions in scientific work (Trieste, 1980) (Bind 4, s. 45-66). D. Reidel Publishing Group.
Barndorff-Nielsen, O. E., Corcuera, J. M. & Podolskij, M. (2011). Multipower variation for Brownian semistationary processes. Bernoulli, 17(4), 1159-1194. https://doi.org/10.3150/10-BEJ316
Bañuelos, R., Baudoin, F., Chen, L. & Sire, Y. (2021). Multiplier theorems via martingale transforms. Journal of Functional Analysis, 281(9), Artikel 109188. https://doi.org/10.1016/j.jfa.2021.109188
Engsner, H., Lindskog, F. & Thøgersen, J. (2023). Multiple-prior valuation of cash flows subject to capital requirements. Insurance: Mathematics and Economics, 111, 41-56. https://doi.org/10.1016/j.insmatheco.2023.02.007
Mrkvička, T., Myllymäki, M. & Hahn, U. (2017). Multiple Monte Carlo Testing, with Applications in Spatial Point Processes. Statistics and Computing, 27(5), 1239–1255. https://doi.org/10.1007/s11222-016-9683-9
Mrkvička, T., Myllymäki, M. & Hahn, U. (2015). Multiple Monte Carlo Testing with Applications in Spatial Point Processes. arxiv.org. http://arxiv.org/abs/1506.01646
Pelizzola, M., Behr, M., Li, H., Munk, A. & Futschik, A. (2021). Multiple haplotype reconstruction from allele frequency data. Nature Computational Science, 1(4), 262-271. https://doi.org/10.1038/s43588-021-00056-5
Basse-O'Connor, A., Graversen, S.-E. & Pedersen, J. (2012). Multiparameter processes with stationary increments: Spectral representation and integration. Electronic Journal of Probability, 17, Article 74. https://doi.org/10.1214/EJP.v17-2287
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. (s. 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., 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), Artikel 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. Datasæt
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. Advance online publication. 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), Artikel 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. I R. Lechnerová, I. Saxl & V. Benes (red.), Proceedings S4G, International Conference on Stereology, Spatial Statistics and Stochastic Geometry (s. 137-147). Union Czech Mathematicians and Physicists.

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Lars Madsen