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Publications

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Iversen, B. B., Jensen, J. L. & Danielsen, J. (1997). Errors in Maximum-Entropy Charge-Density Distributions Obtained from Diffraction Data. Acta Crystallographica Section A: Foundations of Crystallography, 53(3), 376-387. https://doi.org/10.1107/S0108767397000792
Asmussen, S. & Kortschak, D. (2013). Error rates and improved algorithms for rare event simulation with heavy Weibull tails. T.N. Thiele Centre, Department of Mathematics, Aarhus University. Thiele Research Reports No. 02
Rheinbay, E., Nielsen, M. M., Abascal, F., Wala, J. A., Shapira, O., Tiao, G., Hornshøj, H., Hess, J. M., Juul, R. I., Lin, Z., Feuerbach, L., Sabarinathan, R., Madsen, T., Kim, J., Mularoni, L., Shuai, S., Lanzós, A., Herrmann, C., Maruvka, Y. E. ... PCAWG Drivers and Functional Interpretation Working Group (2023). Erratum: Author Correction: Analyses of non-coding somatic drivers in 2,658 cancer whole genomes (Nature (2020) 578 7793 (102-111)). Nature, 614(7948), E40. https://doi.org/10.1038/s41586-022-05599-9
Asmussen, S., Avram, F. & Usabel, M. (2002). Erlangian approximations for finite-horizon ruin probabilities. ASTIN Bulletin: The Journal of the IAA, 32(2), 267-281.
Baudoin, F. & Nualart, D. (2003). Equivalence of Volterra processes. Stochastic Processes and Their Applications, 107(2), 327-350. https://doi.org/10.1016/S0304-4149(03)00088-7
Asmussen, S. (1981). Equilibrium properties of the M/G/1 queue. Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete, 58(2), 267-281.
Asmussen, S., Blanchet, J., Juneja, S. & Rojas-Nandayapa, L. (2008). Efficient simulation of tail probabilities of sums of correlated lognormals. Thiele Centre, Institut for Matematiske Fag, Aarhus Universitet.
Andersen, L. N., Laub, P. & Rojas-Nandayapa, L. (2018). Efficient simulation for dependent rare events with applications to extremes. Methodology and Computing in Applied Probability, 20(1), 385-409. https://doi.org/10.1007/s11009-017-9557-4
Moka, S., Hirsch, C., Schmidt, V. & Kroese, D. (2026). Efficient Rare-Event Simulation for Random Geometric Graphs via Importance Sampling. Methodology and Computing in Applied Probability, 28(2), Article 44. https://doi.org/10.1007/s11009-026-10273-y
Greilich, S., Hahn, U., Kiderlen, M., Andersen, C. E. & Bassler, N. (2014). Efficient calculation of local dose distributions for response modeling in proton and heavier ion beams. The European Physical Journal D: Atomic, Molecular, Optical and Plasma Physics, 68(217), Article 327. https://doi.org/10.1140/epjd/e2014-40825-0
Jensen, J. L. (1997). Efficiency of the pseudo-likelihood estimate in a one-dimensional lattice gas. In Selected Proceedings of the Symposium on Estimating Functions (Athens, GA, 1996) (pp. 369-379). Institute of Mathematical Statistics. https://doi.org/10.1214/lnms/1215455056
Podolskij, M., Veliyev, B. & Yoshida, N. (2015). Edgeworth expansion for the pre-averaging estimator. Institut for Økonomi, Aarhus Universitet. CREATES Research Paper No. 2015-60
Podolskij, M., Veliyev, B. & Yoshida, N. (2017). Edgeworth expansion for the pre-averaging estimator. Stochastic Processes and Their Applications, 127(11), 3558-3595 . https://doi.org/10.1016/j.spa.2017.03.001
Podolskij, M. & Yoshida, N. (2013). Edgeworth expansion for functionals of continuous diffusion processes. Institut for Økonomi, Aarhus Universitet. CREATES Research Paper No. 2013-33
Podolskij, M., Veliyev, B. & Yoshida, N. (2018). Edgeworth expansion for Euler approximation of continuous diffusion processes. Institut for Økonomi, Aarhus Universitet. CREATES Research Paper No. 2018-28