Research
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.
Publications
Dörnemann, N. and Dette, H. (2023+). Linear spectral statistics for sequential sample covariance matrices. To appear, Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques.
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.
Preprints
Dörnemann, N. and Paul, D. (2024). Detecting Spectral Breaks in a Spiked Covariance Model. arXiv:2404.19176.
Dörnemann, N. and Dette, H. (2023). A CLT for the difference of eigenvalue statistics of sample covariance matrices. arXiv:2306.09050.
Dörnemann, N. and Heiny, J. (2022). Limiting spectral distribution for large sample correlation matrices. arXiv:2208.14948.