| 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 | 90 | 2020 |
| Identifying demand effects in a large network of product categories S Gelper, I Wilms, C Croux Journal of Retailing 92 (1), 25-39, 2016 | 61 | 2016 |
| Sparse canonical correlation analysis from a predictive point of view I Wilms, C Croux Biometrical Journal 57 (5), 834-851, 2015 | 54 | 2015 |
| Volatility spillovers in commodity markets: A large t-vector autoregressive approach L Barbaglia, C Croux, I Wilms Energy Economics 85, 104555, 2020 | 47 | 2020 |
| Forecasting using sparse cointegration I Wilms, C Croux International Journal of Forecasting 32 (4), 1256-1267, 2016 | 29 | 2016 |
| Multivariate volatility forecasts for stock market indices I Wilms, J Rombouts, C Croux International Journal of Forecasting, 2020 | 27* | 2020 |
| Robust sparse canonical correlation analysis I Wilms, C Croux BMC systems biology 10 (1), 72, 2016 | 25 | 2016 |
| 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 | 24 | 2016 |
| 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 | 17 | 2018 |
| Commodity dynamics: a sparse multi-class approach L Barbaglia, I Wilms, C Croux Energy Economics 60, 62-72, 2016 | 15 | 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 | 14 | 2021 |
| LASSO inference for high-dimensional time series R Adamek, S Smeekes, I Wilms arXiv preprint arXiv:2007.10952, 2020 | 12 | 2020 |
| An algorithm for the multivariate group lasso with covariance estimation I Wilms, C Croux Journal of Applied Statistics 45 (4), 668-681, 2018 | 11 | 2018 |
| Heteroscedasticity testing after outlier removal V Berenguer-Rico, I Wilms Econometric Reviews 40 (1), 51-85, 2021 | 10* | 2021 |
| 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 | 10 | 2017 |
| Cellwise robust regularized discriminant analysis S Aerts, I Wilms Statistical Analysis and Data Mining: The ASA Data Science Journal 10 (6 …, 2017 | 9 | 2017 |
| Sparse regression for large data sets with outliers L Bottmer, C Croux, I Wilms European Journal of Operational Research, 2021 | 5 | 2021 |
| bigtime: Sparse Estimation of Large Time Series Models I Wilms, S Basu, DS Matteson, J Bien, W Nicholson, E Wegner The Comprehensive R Archive Network, 2021 | 3 | 2021 |
| Discussion of ‘Asymptotic theory of outlier detection algorithms for linear time series regression models’ C Croux, I Wilms Scandinavian Journal of Statistics 43 (2), 353-356, 2016 | 2 | 2016 |
| Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions A Hecq, M Ternes, I Wilms arXiv preprint arXiv:2102.11780, 2021 | 1 | 2021 |
Christophe CrouxEdhec Business SchoolGeverifieerd e-mailadres voor edhec.edu
