Publications
2024
J. Rombouts, M. Ternes and I. Wilms (2024), Cross-temporal forecast reconciliation at digital platforms with machine learning, International Journal of Forecasting, Forthcoming, Open access, Article
Y. J. Hu, J. Rombouts and I. Wilms (2024), Fast forecasting of unstable data streams for on-demand service platforms, Information Systems Research, Forthcoming, Open Access, Article
R. Adamek, S. Smeekes and I. Wilms (2024), Local projection inference in high dimensions, Econometrics Journal, Available Online, Open access , Article
G. Louvet, J. Raymaekers, G. Van Bever and I. Wilms (2024), The influence function of graphical lasso estimators, Econometrics and Statistics, Available Online, Open access, Article
2023
V. Nesrstová, I. Wilms, J. Palarea-Albaladejo, P. Filzmoser, J.A. Martin-Fernández, D. Friedecky, and K. Hron (2023), Principal balances of compositional data for regression and classification using partial least squares, Journal of Chemometrics, 37(12), e3518 Open Access , Article
R. Adamek, S. Smeekes and I. Wilms (2023), Lasso inference for high-dimensional time series, Journal of Econometrics, 235(2), 1114-1143, Open Access , Article
S. Smeekes and I. Wilms (2023), bootUR: An R package for bootstrap unit root tests, Journal of Statistical Software, 106(12), 1-39, Open access
I. Wilms, S. Basu, J. Bien, and D.S. Matteson (2023), Sparse identification and estimation of large-scale vector autoregressive moving averages. Journal of the American Statistical Association, 118(541), 571-582, Open Access, Article
2022
G. Tarr and I. Wilms (2022), Regularized predictive models for beef eating quality of individual meals, Data Science in Science, 1(1), 20-33, Open Access , Article
A. Hecq, M. Ternes and I. Wilms (2022), Hierarchical regularizers for mixed-frequency vector autoregressions, Journal of Computational and Graphical Statistics, 31(4), 1076-1090, Open Access, Article
I. Wilms and J. Bien (2022), Tree-based node aggregation in sparse graphical models, Journal of Machine Learning Research, 23, 1-36, Open Access, Article
I. Wilms, R. Killick, D.S. Matteson (2022), Graphical Influence Diagnostics for Changepoint Models, Journal of Computational and Graphical Statistics, 31(3), 753-765, Open Access, Article
L. Bottmer, C. Croux and I. Wilms (2022), Sparse regression for large data sets with outliers. European Journal of Operational Research, 297(2), 782-794 Open Access, Article
2021
I. Wilms, J. Rombouts, C. Croux (2021), Multivariate volatility forecasts for stock market indices, International Journal of Forecasting, 37(2), 484-499. Open Access, Article
V. Berenguer-Rico and I. Wilms (2021), Heteroscedasticity testing after outlier removal, Econometric Reviews, 40(1), 51-85. Open Access, Article
2020
W.B. Nicholson, I. Wilms, J. Bien and D.S. Matteson (2020), High Dimensional forecasting via interpretable vector autoregression, Journal of Machine Learning Research, 21(166), 1-52. Open Access, Article
L. Barbaglia, C. Croux, and I. Wilms (2020), Volatility spillovers in commodity markets: A large t-vector autoregressive approach, Energy Economics, 85, UNSP 104555. Open Access, Article
2018
I. Wilms, L. Barbaglia, and C. Croux (2018), Multi-class vector autoregressive models for multi-store sales data, Journal of the Royal Statistical Society-Series C, 67(2), 435-452. Open Access, Article
I. Wilms and C. Croux (2018), An algorithm for the multivariate group lasso with covariance estimation, Journal of Applied Statistics, 45(4), 668-681. Open Access, Article
2017
S. Aerts and I. Wilms (2017), Cellwise robust regularized discriminant analysis, Statistical Analysis and Data Mining, 10(6), 436-447. Open Access, Article
I. Wilms, S. Basu, J. Bien and D.S. Matteson (2017), Interpretable vector autoregressions with exogenous time series, NIPS 2017 Symposium on Interpretable Machine Learning, arXiv:1711.03623. Open Access
2016
L. Barbaglia, I. Wilms and C. Croux (2016), Commodity dynamics: A sparse multi-class approach, Energy Economics, 60, 62-72. Open Access, Article
I. Wilms, S. Gelper, and C. Croux (2016), The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach, European Journal of Operational Research, 254(1), 138-147. Open Access, Article
I. Wilms and C. Croux (2016), Forecasting using sparse cointegration, International Journal of Forecasting, 32(4), 1256-1267. Open Access, Article
I. Wilms and C. Croux (2016), Robust sparse canonical correlation analysis, BMC Systems Biology, 10(72), 1-13. Open Access, Article
C. Croux and I. Wilms (2016), Discussion of asymptotic theory of outlier detection algorithms for linear time series regression models, Scandinavian Journal of Statistics, 43(2), 353-356. Open Access, Article
S. Gelper, I. Wilms and C. Croux (2016), Identifying demand effects in a large network of product categories, Journal of Retailing, 92(1), 25-39. Open Access, Article
2015
I. Wilms and C. Croux (2015), Sparse canonical correlation analysis from a predictive point of view, Biometrical Journal, 57(5), 834-851. Open Access, Article