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Dörnemann, N. T. & Dette, H. (2026). A CLT for the difference of eigenvalue statistics of sample covariance matrices. Bernoulli, 32(1), 615-637. https://doi.org/10.3150/25-BEJ1872

Bæk, Z. E. D., Macià, M. C., Skov, L. & Hobolth, A. (2026). Advanced Posterior Analyses of Hidden Markov Models: Finite Markov Chain Imbedding and Hybrid Decoding. Scandinavian journal of statistics. Advance online publication. https://doi.org/10.1111/sjos.70088

Exarchakos, G., van der Hofstad, R., Nagy, O. & Pandey, M. (2026). Bringing order to network centrality measures. (pp. 1-24). ArXiv. https://arxiv.org/abs/2601.16236

Bastian, P., Tieplova, D., Dörnemann, N. & Kutta, T. (2026). Change Point Detection and Localization in High-Dimensional Time Series.

Nagy, O., Pandey, M., Exarchakos, G., Bentum, M. & van der Hofstad, R. (2026). Communication protocol for a satellite-swarm interferometer for low-frequency radio astronomy. Acta Astronautica, 246, 821-831. https://doi.org/10.1016/j.actaastro.2026.04.004

Dörnemann, N. & Paul, D. S. (2026). Detecting spectral breaks in spiked covariance models. Bernoulli, 32(2), 1243-1266. https://doi.org/10.3150/25-BEJ1900

Baudoin, F., Gordina, M. & Sarkar, R. (2026). Dimension-Independent Functional Inequalities by Tensorization and Projection Arguments. Mathematische Nachrichten, 299(7), 1665-1691. https://doi.org/10.1002/mana.70164

Labouriau, R. (2026). Distributional Statistical Models: Weak Moments, Cumulants, and a Central Limit Theorem.

Kutta, T., Dörnemann, N. & Kokoszka, P. (2026). Does PCA Work for Rough Functional Data?

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

Coll Macià, M., Skov, L., Bæk, Z. E. D. & Hobolth, A. (2026). Enhancement of hidden Markov model analyses for improved inference of archaic introgression in modern humans. Molecular Biology and Evolution, 43(6), Article msag134. https://doi.org/10.1093/molbev/msag134

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

Labouriau, R. (2026). From Coefficients to Distributions: De~Moivre and the Operational View of Probability.

Ozkan, R. (2026). Geometric Deep Learning: A Bibliometric Analysis of Research Evolution and Emerging Directions. IEEE Access, 14, 124642-124662. https://doi.org/10.1109/ACCESS.2026.3723132

Gao, J. & Yang, M. (2026). Hölder regularity of harmonic functions on metric measure spaces. Advances in Mathematics, 488, Article 110797. https://doi.org/10.1016/j.aim.2026.110797

Andreis, L., Bassetti, F. & Hirsch, C. (2026). LDP for the covariance process in fully connected Gaussian neural networks. Electronic Journal of Probability, 31, Article 22. https://doi.org/10.1214/26-EJP1477

Hirsch, C. & Owada, T. (2026). Limit theorems under heavy-tailed scenario in the age-dependent random connection models. Stochastic Processes and Their Applications, 192, Article 104815. https://doi.org/10.1016/j.spa.2025.104815

Labouriau, R. (2026). Markov Properties of $k$-Record Processes via Order Statistics.

Ramos-Docampo, M. A., Duan, C., Wang, N., Pandey, M., de Dios Andres, P., Hirsch, C. & Städler, B. (2026). Modeling Active Particle Motion from Fluorescence Correlation Spectroscopy Data. Analytical Chemistry, 98(31), 22781-22791. https://doi.org/10.1021/acs.analchem.6c01438

Labouriau, R. (2026). Notes on Transversality and Statistical Degeneracies in Distributional Models.

Asmussen, S. (2026). On Erlangization and Extrapolation, with Applications to Financial Regime-Switching Lévy Models. Methodology and Computing in Applied Probability, 28(1), Article 19. https://doi.org/10.1007/s11009-025-10234-x

Pérez-Abreu, V. & Ørnsen, S. T. (2026). On Ole E. Barndorff-Nielsen’s contributions to classical and free infinite divisibility and Lévy processes. Bernoulli, 32(1), 5-23. https://doi.org/10.3150/25-BEJ1896

Baudoin, F., Chen, L., Huang, C. H., Ouyang, C., Tindel, S. & Wang, J. (2026). Parabolic Anderson model in bounded domains of recurrent metric measure spaces. Transactions of the American Mathematical Society, 379(3), 1799-1851. https://doi.org/10.1090/tran/9540

Sendrowski, J. & Hobolth, A. (2026). PhaseGen: exact solutions for time-inhomogeneous multivariate coalescent distributions under diverse demographies. Genetics, 232(1), Article iyaf135. https://doi.org/10.1093/genetics/iyaf135

Hirsch, C., Lundbye, N. N. & Otto, M. (2026). Poisson approximation of large-lifetime cycles. Extremes, 29(3), 495-559. https://doi.org/10.1007/s10687-026-00526-x

Chen, H. & Lee, C. Y. (2026). Propagation of singularities for the damped stochastic Klein-Gordon equation. Stochastics and Partial Differential Equations: Analysis and Computations. Advance online publication. https://doi.org/10.1007/s40072-026-00433-z

Baudoin, F., Kuijper, T. & Wang, J. (2026). Quaternionic Stochastic Areas on Quaternionic Full Flag Manifolds and Applications. Journal of Theoretical Probability, 39(3), Article 52. https://doi.org/10.1007/s10959-026-01514-4

Brun, M., Hirsch, C., Juhász, P. & Otto, M. (2026). Random connection hypergraphs. Stochastic Models, 42(3), 345-387. https://doi.org/10.1080/15326349.2026.2651174

Holk, A., Strauch, C. & Trottner, L. (2026). Reflected diffusion models adapt to low-dimensional data.

Bendikov, A., Chen, L. & Saloff-Coste, L. (2026). Riesz Transforms, Function Spaces, and Their Applications on Infinite Dimensional Compact Groups. Potential Analysis, 64(4), Article 60. https://doi.org/10.1007/s11118-026-10288-7

Chen, A. & Yu, Z. (2026). Spectral Bounds and Heat Kernel Upper Estimates for Dirichlet Forms. Mathematische Nachrichten, 299(9), 2566-2589. Article e70207. https://doi.org/10.1002/mana.70207

Holk, A., Strauch, C. & Trottner, L. (2026). Statistical guarantees for denoising reflected diffusion models.

Baudoin, F. (2026). Sub-Laplacian comparison theorems on Riemannian foliations with minimal leaves and applications. Communications on Pure and Applied Analysis, 32, 192-214. https://doi.org/10.3934/cpaa.2026035

Baudoin, F. & Yang, G. (2026). Sub-Laplacian generalized curvature dimension inequalities on Riemannian foliations.

Dörnemann, N., Fleermann, M. & Heiny, J. (2026). Ties, tails and spectra: On rank-based dependency measures in high dimensions. Stochastic processes and their applications, 201, Article 105055. https://doi.org/10.1016/j.spa.2026.105055

Labouriau, R. (2026). Transversality and Geometric Regularisation in Distributional Statistical Models.

Dörnemann, N. (2026). Two-sample covariance inference in high-dimensional elliptical models. Electronic Journal of Statistics, 20(1), 633-666. https://doi.org/10.1214/26-EJS2499

Jensen, J. L., Mortensen, P. B. & Serritzlew, S. (2026). Understanding Policy Changes: The Mixture Model of Politics. Oxford University Press. https://doi.org/10.1093/9780197900413.001.0001

Labouriau, R. (2026). Weak Information Geometry: Riemannian Structures from Distributional Inference Functions and Stein Discrepancies.

Labouriau, R. (2026). Weak Moment Methods for Statistical Inference: with an Application to Robust Estimation.

Dare, E., Kiderlen, M. & Thäle, C. (2025). A Blaschke–Petkantschin formula for linear and affine subspaces with application to intersection probabilities. Nonlinear Analysis, Theory, Methods and Applications, 250, Article 113672. https://doi.org/10.1016/j.na.2024.113672

Ramos Docampo, M. A., Abild Meyer, C., Ryberg, C., Otzen, D. E., Hirsch, C. & Städler, B. (2025). Actin Polymerizing Motors to Assist Cytoskeleton-like Networks Formation in Artificial Cells. ACS Nano, 19(42), 37324-37338. https://doi.org/10.1021/acsnano.5c12624

Hobolth, A., Boitard, S., Futschik, A. & Leblois, R. (2025). A matrix-analytical sampling formula for time-homogeneous coalescent processes under the infinite sites mutation model. Theoretical Population Biology, 163, 62-79. https://doi.org/10.1016/j.tpb.2025.03.002

Lam, T., Dörnemann, N. & Dette, H. (2025). A New Two-Sample Test for Covariance Matrices in High Dimensions: U-Statistics Meet Leading Eigenvalues.

Kutta, T. (2025). Approximately mixing time series. Statistics and Probability Letters, 220, Article 110360. https://doi.org/10.1016/j.spl.2025.110360

Pandey, M. & Hofstad, R. V. D. (2025). Are giants in random digraphs ‘almost’ local? Electronic Communications in Probability, 30, 1. Article 48. https://doi.org/10.1214/25-ECP694

Kuijper, T. (2025). Brownian motion and stochastic areas on complex partial flag manifolds with blocks of equal size.

Pandey, M. (2025). Centrality Measures and Connectivity Properties in Large Networks: Who is the most influential in a network? [PhD dissertation, Eindhoven University of Technology]. Eindhoven Technical University. https://research.tue.nl/en/publications/centrality-measures-and-connectivity-properties-in-large-networks

Baudoin, F., Grong, E., Rizzi, L. & Vega-Molino, S. (2025). Comparison theorems on H-type sub-Riemannian manifolds. Calculus of Variations and Partial Differential Equations, 64(5), Article 143. https://doi.org/10.1007/s00526-025-02992-w

Stehr, M. & Kiderlen, M. (2025). Confidence intervals for Newton–Cotes quadratures based on stationary point processes. Statistical Methods & Applications, 34(1), 39-67. https://doi.org/10.1007/s10260-024-00773-x

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Lars Madsen