| Nonparametric C-and D-vine-based quantile regression M Tepegjozova, J Zhou, G Claeskens, C Czado Dependence Modeling 10 (1), 1-21, 2022 | 14 | 2022 |
| Composite versus model-averaged quantile regression D Bloznelis, G Claeskens, J Zhou Journal of Statistical Planning and Inference 200, 32-46, 2019 | 10 | 2019 |
| Automatically identifying relevant variables for linear regression with the Lasso method: a methodological primer for its application with R and a performance contrast … S Scherr, J Zhou Communication Methods and Measures 14 (3), 204-211, 2020 | 5 | 2020 |
| Detangling robustness in high dimensions: Composite versus model-averaged estimation J Zhou, G Claeskens, J Bradic | 5 | 2020 |
| A tradeoff between false discovery and true positive proportions for sparse high-dimensional logistic regression J Zhou, G Claeskens Electronic Journal of Statistics 18 (1), 395-428, 2024 | 1 | 2024 |
| High-dimensional Newey-Powell Test Via Approximate Message Passing J Zhou, H Zou arXiv preprint arXiv:2311.05056, 2023 | | 2023 |
| Discussion on:“A scale-free approach for false discovery rate control in generalized linear models” by Dai, Lin, Zing, Liu G Claeskens, M Jansen, J Zhou Journal of the American Statistical Association 118 (543), 1573-1577, 2023 | | 2023 |
| Automatic bias correction for testing in high‐dimensional linear models J Zhou, G Claeskens Statistica Neerlandica 77 (1), 71-98, 2023 | | 2023 |
| Weight choice for penalized composite quantile regression and for model averaging J Zhou, G Claeskens, D Bloznelis Proceedings of the 33rd International Workshop on Statistical Modelling 2 …, 2018 | | 2018 |
| High dimensional quantile regression: model averaging and composite estimation J Zhou | | |
Marija TepegjozovaPhD studentVerified email at tum.de
