My research is been primarily focused on random matrix theory and its applications in high-dimensional statistics.
Broadly, my research interests encompass asymptotic statistics, central limit theorems, and hypothesis testing in high-dimensional contexts.
Specifically, my work has involved the study of the spectral properties of multivariate dependency measures and the investigation of likelihood ratio and change-point tests for large dimensional covariance matrices.
Dörnemann, N. (2026). Two-Sample Covariance Inference in High-Dimensional Elliptical Models. Electronic Journal of Statistics, Volume 20(1): 633-666.
Dörnemann, N. and Paul, D. (2026). Detecting Spectral Breaks in a Spiked Covariance Model. Bernoulli, Vol. 32, No. 2, 1243-1266
Dörnemann, N. and Dette, H. (2026). A CLT for the difference of eigenvalue statistics of sample covariance matrices. Bernoulli,, Vol. 32, No. 1, 615-637.
Dörnemann, N. and Heiny, J. (2025). Limiting spectral distribution for large sample correlation matrices. Annals of Applied Probability, Vol. 35, No. 4, 2570-2603.
Kutta, T. and Dörnemann, N. (2025). Monitoring Time Series with Short Detection Delay. Electronic Journal of Statistics, Vol. 19, No. 1, 2239-2275.
Dörnemann, N. and Dette, H. (2024). Linear spectral statistics for sequential sample covariance matrices. Annales de l'Institut Henri Poincaré - Probabilités et Statistiques. Vol. 60, No. 2, 946-970.
Dörnemann, N. and Dette, H. (2023). Fluctuations of the diagonal entries of a large sample precision matrix. Statistics and Probability Letters, 198:109838.
Dörnemann, N. (2023). Likelihood ratio tests under model misspecification in high dimensions. Journal of Multivariate Analysis, 193:105122.
Dörnemann, N. (2023). Asymptotics for linear spectral statistics of sample covariance matrices. Dissertation, Ruhr University Bochum.
Dette, H. and Dörnemann, N. (2020): Likelihood ratio tests for many groups in high dimensions. Journal of Multivariate Analysis 178, 104605.
Dörnemann, N., Fleermann, M. and Heiny, J. (2025). Ties, Tails and Spectra: On Rank-Based Dependency Measures in High Dimensions, arxiv:2506.14992.
Dörnemann, N., Kokoszka P., Kutta T. and Lee, S. (2025). Monitoring for a Phase Transition in a Time Series of Wigner Matrices. arxiv:2507.04983.
Lam, T., Dörnemann, N. and Dette, H. (2025). A New Two-Sample Test for Covariance Matrices in High Dimensions: U-Statistics Meet Leading Eigenvalues. arXiv:2506.06550.
Dörnemann, N. and Lopes, M. E. (2025). Tracy-Widom, Gaussian, and Bootstrap: Approximations for Leading Eigenvalues in High-Dimensional PCA. arXiv:2503.23097.
Dörnemann, N. and Dette, H. (2024). Detecting Change Points of Covariance Matrices in High Dimensions. arXiv:2409.15588.