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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
902020
Identifying demand effects in a large network of product categories
S Gelper, I Wilms, C Croux
Journal of Retailing 92 (1), 25-39, 2016
612016
Sparse canonical correlation analysis from a predictive point of view
I Wilms, C Croux
Biometrical Journal 57 (5), 834-851, 2015
542015
Volatility spillovers in commodity markets: A large t-vector autoregressive approach
L Barbaglia, C Croux, I Wilms
Energy Economics 85, 104555, 2020
472020
Forecasting using sparse cointegration
I Wilms, C Croux
International Journal of Forecasting 32 (4), 1256-1267, 2016
292016
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
252016
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
242016
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
172018
Commodity dynamics: a sparse multi-class approach
L Barbaglia, I Wilms, C Croux
Energy Economics 60, 62-72, 2016
152016
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
142021
LASSO inference for high-dimensional time series
R Adamek, S Smeekes, I Wilms
arXiv preprint arXiv:2007.10952, 2020
122020
An algorithm for the multivariate group lasso with covariance estimation
I Wilms, C Croux
Journal of Applied Statistics 45 (4), 668-681, 2018
112018
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
102017
Cellwise robust regularized discriminant analysis
S Aerts, I Wilms
Statistical Analysis and Data Mining: The ASA Data Science Journal 10 (6 …, 2017
92017
Sparse regression for large data sets with outliers
L Bottmer, C Croux, I Wilms
European Journal of Operational Research, 2021
52021
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
32021
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
22016
Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions
A Hecq, M Ternes, I Wilms
arXiv preprint arXiv:2102.11780, 2021
12021
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Artikelen 1–20