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. and Paul, D. (2025+). Detecting Spectral Breaks in a Spiked Covariance Model. Bernoulli, to appear.
Dörnemann, N. and Dette, H. (2025+). A CLT for the difference of eigenvalue statistics of sample covariance matrices. Bernoulli, to appear.
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.
Dörnemann, N. (2025). Two-Sample Covariance Inference in High-Dimensional Elliptical Models. arxiv:2507.02640.
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.