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Jensen, J. L. (2011). On a saddlepoint approximation to the Markov binomial distribution. Thiele Centre, Institut for Matematiske Fag, Aarhus Universitet.

Jensen, J. L. (2013). On a saddlepoint approximation to the Markov binomial distribution. Brazilian Journal of Probability and Statistics, 27(2), 150-161. https://doi.org/10.1214/11-BJPS162

Kiderlen, M. & Pausinger, F. (2022). On a partition with a lower expected L₂-discrepancy than classical jittered sampling. Journal of Complexity, 70, Article 101616. https://doi.org/10.1016/j.jco.2021.101616

Andersen, K., Wagner, C. & Weismantel, R. (2009). On an analysis of the strength of mixed integer cutting planes from multiple simplex tableau rows. S I A M Journal on Optimization, 20/2, 967-982. https://doi.org/10.1137/080744360

Thirstrup, J. P., Labouriau, R., Guldbrandtsen, B., Anistoroaei, R. M., Christensen, K., Fredholm, M. & Nielsen, V. H. (2011). Ny jagt på pelsgener. In P. Berg (Ed.), Temadag om aktuel minkforskning (pp. 75-80). Aarhus Universitet, Forskningscenter Foulum.

Lamandé, M., Keller, T., Labouriau, R. & Schjønning, P. (2012). Numerical method for determination of point of maximum curvature on soil compression curves from unixial confined compression tests. Abstract from 19th International Conference of the International Soil Tillage Research Organization, Montevideo, Uruguay.

Baudoin, F. & Nualart, D. (2006). Notes on the two-dimensional fractional Brownian motion. Annals of Probability, 34(1), 159-180. https://doi.org/10.1214/009117905000000288

Dormann, F., Frisk, O., Andersen, L. N. & Pedersen, C. F. (2021). Not All Noise Is Accounted Equally: How Differentially Private Learning Benefits From Large Sampling Rates. In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021 IEEE. https://doi.org/10.1109/MLSP52302.2021.9596307

Andersen, C. L., Jensen, J. L. & Ørntoft, T. F. (2004). Normalization of real-time quantitative reverse transcription-PCR data: "a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets" Cancer Resarch, 64(15), 5245-5250. http://cancerres.aacrjournals.org/cgi/content/full/64/15/5245

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. In C. Babu Rao, P. Kalyanasundaram, KK. Ray & B. Raj (Eds.), Image Analysis in Materials and Life Sciences (pp. 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), Article 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), Article 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, Article 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 from 5th Annual Research Meeting at the Department of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark.

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. (Eds.) (1993). Networks and chaos: statistical and probabilistic aspects.

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. (pp. 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), Article 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 from 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. In Statistical distributions in scientific work (Trieste, 1980) (Vol. 4, pp. 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), Article 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. (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

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Lars Madsen