| High Dimensional Forecasting via Interpretable Vector Autoregression. WB Nicholson, I Wilms, J Bien, DS Matteson Journal of Machine Learning Research 21 (166), 1-52, 2020 | 179 | 2020 |
| Volatility spillovers in commodity markets: A large t-vector autoregressive approach L Barbaglia, C Croux, I Wilms Energy Economics 85, 104555, 2020 | 90 | 2020 |
| Sparse canonical correlation analysis from a predictive point of view I Wilms, C Croux Biometrical Journal 57 (5), 834-851, 2015 | 79 | 2015 |
| Identifying demand effects in a large network of product categories S Gelper, I Wilms, C Croux Journal of Retailing 92 (1), 25-39, 2016 | 74 | 2016 |
| LASSO inference for high-dimensional time series R Adamek, S Smeekes, I Wilms Journal of Econometrics 235 (2), 1114-1143, 2023 | 55 | 2023 |
| Multivariate volatility forecasts for stock market indices I Wilms, J Rombouts, C Croux International Journal of Forecasting 37 (2), 484-499, 2021 | 55* | 2021 |
| Robust sparse canonical correlation analysis I Wilms, C Croux BMC systems biology 10 (1), 1-13, 2016 | 48 | 2016 |
| Sparse regression for large data sets with outliers L Bottmer, C Croux, I Wilms European Journal of Operational Research 297 (2), 782-794, 2022 | 44 | 2022 |
| Forecasting using sparse cointegration I Wilms, C Croux International Journal of Forecasting 32 (4), 1256-1267, 2016 | 41 | 2016 |
| Heteroscedasticity testing after outlier removal V Berenguer-Rico, I Wilms Econometric Reviews 40 (1), 51-85, 2021 | 34 | 2021 |
| The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach I Wilms, S Gelper, C Croux European Journal of Operational Research 254 (1), 138-147, 2016 | 26 | 2016 |
| Sparse identification and estimation of large-scale vector autoregressive moving averages I Wilms, S Basu, J Bien, DS Matteson Journal of the American Statistical Association, 1-12, 2021 | 25 | 2021 |
| Multi-class vector autoregressive models for multi-store sales data I Wilms, L Barbaglia, C Croux Journal of the Royal Statistical Society-Series C 67 (2), 435-452, 2018 | 23 | 2018 |
| Commodity dynamics: a sparse multi-class approach L Barbaglia, I Wilms, C Croux Energy Economics 60, 62-72, 2016 | 21 | 2016 |
| An algorithm for the multivariate group lasso with covariance estimation I Wilms, C Croux Journal of Applied Statistics 45 (4), 668-681, 2018 | 19 | 2018 |
| Cellwise robust regularized discriminant analysis S Aerts, I Wilms Statistical Analysis and Data Mining: The ASA Data Science Journal 10 (6 …, 2017 | 15 | 2017 |
| Interpretable vector autoregressions with exogenous time series I Wilms, S Basu, J Bien, DS Matteson NIPS 2017 Symposium on Interpretable Machine Learning, arXiv:, 1711.03623, 2017 | 13 | 2017 |
| Local Projection Inference in High Dimensions R Adamek, S Smeekes, I Wilms The Econometrics Journal, 2024 | 10 | 2024 |
| bootUR: An R Package for Bootstrap Unit Root Tests S Smeekes, I Wilms Journal of Statistical Software 106 (12), 1-39, 2023 | 8 | 2023 |
| Tree-based Node Aggregation in Sparse Graphical Models I Wilms, J Bien Journal of Machine Learning Research 23, 1-36, 2022 | 6 | 2022 |
Christophe CrouxKU Leuven - Faculty of Economics and Business確認したメール アドレス: kuleuven.be