Ongoing Research

• 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

• P. Puchhammer, I. Wilms and P. Filzmoser, Sparse outlier-robust PCA for multi-source data, arXiv:2407.16299

• A. Hecq, I. Ricardo and I. Wilms, Reduced-rank matrix autoregressive models: A medium N Approach, arXiv:2407.07973

• D.J.W. Touw, A. Alfons, P.J.F. Groenen and I. Wilms, Clusterpath Gaussian graphical modeling, arXiv:2407.00644

• 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