Ongoing Research

• D. Girolimetto, J. Rombouts, I. Wilms and Y.F. Yang, FoReco and FoRecoML: A unified toolbox for forecast reconciliation in R, arXiv:2604.27696

• T. Sadhukhan, I. Wilms, S. Smeekes and S. Basu, Autotune: fast, accurate and automatic tuning parameter selection for LASSO, arXiv:2512.11139

• R. Shankar, I. Wilms, J. Raymaekers and G. Tarr, Robust outlier-adjusted mean-shift estimation of state space models, arXiv:2511.15155 

• A. Hecq, I. Ricardo and I. Wilms, Decomposing co-movements in matrix-valued time series: A pseudo-structural reduced-rank approach arXiv:2509.19911

• P. Puchhammer, I. Wilms and P. Filzmoser, A smooth multi-group Gaussian mixture model for cellwise robust covariance estimation, arXiv:2504.02547

• Y.J. Hu, J. Rombouts and I. Wilms, MLOps monitoring at scale for digital platforms, arXiv:2504.16789.

• P. Haimerl, S. Smeekes and I. Wilms, Estimation of latent group structures in time-varying panel data models, arXiv:2503.23165 

• A. Archimbaud, A. Alfons and I. Wilms, Robust matrix completion for discrete rating-scale data, arXiv:2412.20802

• E. Wegner, L. Lieb, S. Smeekes and I. Wilms, Transmission channel analysis in dynamic models, arXiv:2405.18987

• R. Adamek, S. Smeekes and I. Wilms, Sparse high-dimensional vector autoregressive bootstrap, arXiv:2302.01233