ines wilms
ines wilms
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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
Identifying demand effects in a large network of product categories
S Gelper, I Wilms, C Croux
Journal of Retailing 92 (1), 25-39, 2016
Sparse canonical correlation analysis from a predictive point of view
I Wilms, C Croux
Biometrical Journal 57 (5), 834-851, 2015
Volatility spillovers in commodity markets: A large t-vector autoregressive approach
L Barbaglia, C Croux, I Wilms
Energy Economics 85, 104555, 2020
Forecasting using sparse cointegration
I Wilms, C Croux
International Journal of Forecasting 32 (4), 1256-1267, 2016
Multivariate volatility forecasts for stock market indices
I Wilms, J Rombouts, C Croux
International Journal of Forecasting 37 (2), 484-499, 2021
Robust sparse canonical correlation analysis
I Wilms, C Croux
BMC systems biology 10 (1), 1-13, 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
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
Commodity dynamics: a sparse multi-class approach
L Barbaglia, I Wilms, C Croux
Energy Economics 60, 62-72, 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
Heteroscedasticity testing after outlier removal
V Berenguer-Rico, I Wilms
Econometric Reviews 40 (1), 51-85, 2021
LASSO inference for high-dimensional time series
R Adamek, S Smeekes, I Wilms
arXiv preprint arXiv:2007.10952, 2020
An algorithm for the multivariate group lasso with covariance estimation
I Wilms, C Croux
Journal of Applied Statistics 45 (4), 668-681, 2018
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
Sparse regression for large data sets with outliers
L Bottmer, C Croux, I Wilms
European Journal of Operational Research 297 (2), 782-794, 2022
Cellwise robust regularized discriminant analysis
S Aerts, I Wilms
Statistical Analysis and Data Mining: The ASA Data Science Journal 10 (6 …, 2017
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
The Influence Function of Graphical Lasso Estimators
G Louvet, J Raymaekers, G Van Bever, I Wilms
arXiv preprint arXiv:2209.07374, 2022
Hierarchical regularizers for mixed-frequency vector autoregressions
A Hecq, M Ternes, I Wilms
Journal of Computational and Graphical Statistics 31 (4), 1076-1090, 2022
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