Veröffentlichungen

Publications:

  • Behr, M. and Munk, A. (2022). Statistical Methods for Minimax Estimation in Linear Models with Unknown Design Over Finite Alphabets. SIMODS (Vol. 4, Issue 2) [here]
  • Dörnemann, N. and Dette, H. (2022). Fluctuations of the diagonal entries of a large sample precision matrix. Statistics & Probability Letters (Vol. 198) [here]
  • Dörnemann, N. (2022). Likelihood ratio tests under model misspecification in high dimensions. Journal of Multivariate Analysis (Vol. 193) [here]
  • Gaucher, S. and Carpentier, A. and Giraud, C. (2022) The price of unfairness in linear bandits with biased feedback. Advances in Neural Information Processing Systems 35, pp. 18363–18376. [here]
  • Dasgupta, S. and Mukhopadhyay, S. and Kieth, J. (2023). G-optimal grid designs for kriging models. Scandinavian Journal of Statistics [here]
  • Butucea, C. and Rohde, A. and Steinberger, L. (2023). Interactive versus non-interactive locally differentially private estimation: Two elbows for the quadratic functional. Annals of Statistics 51(2), 464-486. [here]
  • Steinberger, L. and Leeb, H. (2023). Conditional predictive inference for stable algorithms. Annals of Statistics 51(1), 290–311. [here]
  • Hucker, L., Wahl, M. (2023). A note on the prediction error of principal component regression in high dimensions. Theory of Probability and Mathematical Statistics 109, 37–53. [here]
  • Kovács, S. and Bühlmann, P. and Li, H. and Munk A. (2023) Seeded binary segmentation: a general methodology for fast and optimal changepoint detection. Biometrika 110, 249-256 [here]
  • Kocak, T. and Carpentier, A. (2023) Online Learning with Feedback Graphs: The True Shape of Regret. International Conference on Machine Learning. PMLR. 2023, pp. 17260–17282. [here]
  • Pilliat, E. and Carpentier, A. and Verzelen, N. (2023) Optimal Permutation Estimation in CrowdSourcing problems. The Annals of Statistics 51.3, pp. 935–961. [here]
  • Saad, E. M. and Verzelen, N. and Carpentier, A. (2023) Active Ranking of Experts Based on their Performances in Many Tasks. International Conference on Machine Learning. PMLR. 2023, pp. 29490–29513. [here]
  • Jirak, M. and Wahl, M. (2023) Relative perturbation bounds with applications to empirical covariance operators. Advances in Mathematics 412, 108808 [here]
  • Pilliat, E. and Carpentier, A. and Verzelen, N. (2024) Optimal rates for ranking a permuted isotonic matrix in polynomial time. Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). SIAM. 2024, pp. 3236–3273. [here]
  • Strothmann, C. and Dette, H. and Siburg, K. F. (2024) Rearranged dependence measures. Bernoulli 30(2),pp. 1055-1078 [here]
  • Delft, A.v. and Dette, H. (2024). A general framework to quantify deviations from structural assumptions in the analysis of nonstationary function-valued processes. Annals of Statistics 52 (2), 550-579 [here]
  • Bastian, P. and Dette, H. and Heiny, J. (2024). Testing for practically significant dependencies in high dimensions via bootstrapping maxima of U-statistics. Annals of Statistics 52 (2), 628-653 [here]
  • Blanchard G. and Carpentier, A. and Zadorozhnyi, O. (2024) Moment inequalities for sums of weakly dependent random fields. Bernoulli 30(3): 2501-2520 [here]
  • Dette, H. and Tang, J. (2022). An RKHS approach for pivotal inference in functional linear regression. Statistica Sinica [here]
  • Brutsche, J. and Rohde, A. (2022). Sharp adaptive similarity testing with pathwise stability for ergodic diffusions. Annals of Statistics [here]
  • Jirak, M. and Wahl, M. (2022) Quantitative limit theorems and bootstrap approximations for empirical spectral projectors. Probability Theory and Related Fields [here]
  • Dette, H. and Dierickx, G. and Kutta, T. (2023). Testing covariance separability for continuous functional data [here]
  • Dette, H. and Kroll, M. (2023). A Simple Bootstrap for Chatterjee’s Rank Correlation. Biometrika [here]

To Appear:

  • Chhor, J. and Carpentier, A. (2022) Goodness-of-fit testing for Hölder-continuous densities: Sharp local minimax rates. Bernoulli [here]
  • Beckedorf, P. and Rohde, A. (2022). Non-uniform bounds and Edgeworth expansions in self-normalized limit theorems. Journal of Theoretical Probability [here]
  • Steinberger, L. (2023). Efficiency in local differential privacy. Annals of Statistics [here]

Preprints:

  • Deitmar, B. (2022). Trace Moments of the Sample Covariance Matrix with Graph-Coloring (Preprint)
  • Dörnemann, N. and Heiny, J. (2022). Limiting spectral distribution for large sample correlation matrices (Preprint)
  • Kovács, S. and Li, H. and Haubner, L. and Munk, A. and Bühlmann, P. (2022) Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries. (Preprint)
  • Dörnemann, N. and Dette, H. (2023). A CLT for the difference of eigenvalue statistics of sample covariance matrices (Preprint)
  • Dasgupta, S. and Dette, H. (2023). Efficient subsampling for exponential family models (Preprint)
  • Mösching, A. and Li, H. and Munk, A. (2023) Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models. (Preprint)
  • Ai, M., Dette, H., Liu, Z., Yu, J. (2023) A reinforced learning approach to optimal design under model uncertainty. (Preprint)
  • Köstenberger, G. and Stark, T. (2024). Robust signal recovery in Hadamard spaces. (Preprint)
  • Kalinin, N. and Steinberger L. (2024). Efficient estimation of a Gaussian mean with local differential privacy. (Preprint)
  • Amann, N. and Leeb, H. and Steinberger, L. (2024). Assumption-lean conditional predictive inference via the Jackknife and the Jackknife+. (Preprint)
  • Deitmar, B. (2024) A recursion formula for mixed trace moments of isotropic Wishart matrices and the Gaussian unitary/orthogonal ensembles (Preprint)
  • Dette, H. and Tang, J. (2024) New energy distances for statistical inference on infinite dimensional Hilbert spaces without moment conditions (Preprint)
  • Döremann, N. and Paul, D. (2024). Detecting Spectral Breaks in Spiked Covariance Models (Preprint)
  • Hucker, L. and Reiß, M. (2024). Early stopping for conjugate gradients in statistical inverse problems (Preprint)
  • Tang, J. and Dette, H. (2024). Simultaneous semiparametric inference for single-index models (Preprint)
  • Dette, H. and Rohde, A. (2024). Nonparametric bootstrap of high-dimensional sample covariance matrices (Preprint)
  • Chen, H., Dette, H. and Yu, J. (2024). Multi-resolution subsampling for large-scale linear classification (Preprint)
  • Bastian, P. and Dette, H. (2024). Gradual changes in functional time series (Preprint)
  • Deitmar, B. (2024). High-dimensional Gaussian linear processes: Marchenko-Pastur beyond simultaneous diagonalizability (Preprint)
  • Graf, M., Carpentier, A. and Verzelen, N. (2024). Optimal level set estimation for non-parametric tournament and crowdsourcing problems (Preprint)
  • Dörnemann, N., Dette, H. (2024) Detecting Change Points of Covariance Matrices in High Dimensions (Preprint)
  • Butucea, C., Meister, A., Rohde, A. (2024) A Unifying Framework for Global Gaussianization: Asymptotic Equivalence of Locally Stationary Processes and Bivariate White Noise (Preprint)