| Bootstrap based inference for sparse high-dimensional time series models J Krampe, JP Kreiss, E Paparoditis | 22 | 2021 |
| Injury risk functions for frontal oblique collisions N Andricevic, M Junge, J Krampe Traffic injury prevention 19 (5), 518-522, 2018 | 16 | 2018 |
| Estimated Wold representation and spectral-density-driven bootstrap for time series J Krampe, JP Kreiss, E Paparoditis Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2018 | 14 | 2018 |
| Optimising casing milling Rate Of Penetration (ROP) by applying the concept of Mechanical Specific Energy (MSE): a justification of the concept's applicability by literature … R Suppes, A Ebrahimi, J Krampe Journal of Petroleum Science and Engineering 180, 918-931, 2019 | 13 | 2019 |
| Extrapolation of GIDAS accident data to Europe JP Kreiss, G Feng, J Krampe, M Meyer, T Niebuhr, C Pastor, ... | 11 | 2015 |
| Injury severity for hazard & risk analyses: calculation of ISO 26262 S-parameter Values from Real-World Crash Data J Krampe, M Junge Accident Analysis & Prevention 138, 105321, 2020 | 9 | 2020 |
| Sparsity concepts and estimation procedures for high‐dimensional vector autoregressive models J Krampe, E Paparoditis Journal of Time Series Analysis 42 (5-6), 554-579, 2021 | 8 | 2021 |
| Time series modeling on dynamic networks J Krampe | 8 | 2019 |
| Structural inference in sparse high-dimensional vector autoregressions J Krampe, E Paparoditis, C Trenkler Journal of Econometrics 234 (1), 276-300, 2023 | 5 | 2023 |
| Frequency domain statistical inference for high-dimensional time series J Krampe, E Paparoditis arXiv preprint arXiv:2206.02250, 2022 | 5 | 2022 |
| Factor Models with Sparse VAR Idiosyncratic Components J Krampe, L Margaritella arXiv preprint arXiv:2112.07149, 2021 | 5 | 2021 |
| Impulse response analysis for sparse high-dimensional time series J Krampe, E Paparoditis, C Trenkler arXiv preprint arXiv:2007.15535, 2020 | 5 | 2020 |
| Deriving functional safety (ISO 26262) S-parameters for vulnerable road users from national crash data J Krampe, M Junge Accident Analysis & Prevention 150, 105884, 2021 | 4 | 2021 |
| Hybrid wild bootstrap for nonparametric trend estimation in locally stationary time series J Krampe, JP Kreiss, E Paparoditis Statistics & Probability Letters 101, 54-63, 2015 | 3 | 2015 |
| Estimating wold matrices and vector moving average processes J Krampe, TL McMurry Journal of Time Series Analysis 42 (2), 201-221, 2021 | 2 | 2021 |
| Inverse covariance operators of multivariate nonstationary time series J Krampe, S Subba Rao Bernoulli 30 (2), 1177-1196, 2024 | 1 | 2024 |
| Population-based assessment of a vehicle fleet with seat belts providing lower shoulder belt forces than today J Krampe, M Junge Traffic injury prevention 20 (3), 320-324, 2019 | 1 | 2019 |
| Hochrechnung von GIDAS auf das Unfallgeschehen in Deutschland JP Kreiß, G Feng, J Krampe, M Meyer, T Niebuhr FAT Forschungsvereinigung Automobiltechnik, 2015 | 1 | 2015 |
| Global bank network connectedness revisited: What is common, idiosyncratic and when? J Krampe, L Margaritella arXiv preprint arXiv:2402.02482, 2024 | | 2024 |
| Estimation of the Distribution of the Individual Reproduction Number: The Case of the COVID-19 Pandemic A Braumann, J Krampe, JP Kreiss, E Paparoditis arXiv preprint arXiv:2101.07919, 2021 | | 2021 |
