Ines Wilms (I.)

Kirch, C., Lahiri, S., Binder, H., Brannath, W., Cribben, I., Dette, H., Doebler, P., Feng, O., Gandy, A., Greven, S., Hammer, B., Harmeling, S., Hotz, T., Kauermann, G., Krause, J., Krempl, G., Nieto-Reyes, A., Okhrin, O., Ombao, H., ... Lederer, J. (2025). Challenges and Opportunities for Statistics in the Era of Data Science. Harvard Data Science Review, 7(2). https://doi.org/10.1162/99608f92.abf14c9d

Hecq, A., Ternes, M., & Wilms, I. (2025). Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions. Journal of Forecasting. Advance online publication. https://doi.org/10.1002/for.3277

Hecq, A., Ricardo, I., & Wilms, I. (2025). Detecting cointegrating relations in non-stationary matrix-valued time series. Economics Letters, 248, Article 112205. https://doi.org/10.1016/j.econlet.2025.112205

Hu, Y. J., Rombouts, J., & Wilms, I. (2025). Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms. Information Systems Research, 36(1), 552-571. https://doi.org/10.1287/isre.2023.0130

Düker, M.-C., Matteson, D. S., Tsay, R. S., & Wilms, I. (2025). Vector autoregressive moving average models: A review. WIREs Computational Statistics, 17(1), Article e70009. https://doi.org/10.1002/wics.70009

Nesrstová, V., Wilms, I., Hron, K., & Filzmoser, P. (2025). Identifying Important Pairwise Logratios in Compositional Data with Sparse Principal Component Analysis. Mathematical Geosciences, 57(2), 333-358. https://doi.org/10.1007/s11004-024-10159-0

Rombouts, J., Ternes, M., & Wilms, I. (2025). Cross-temporal forecast reconciliation at digital platforms with machine learning. International Journal of Forecasting, 41(1), 321-344. https://doi.org/10.1016/j.ijforecast.2024.05.008

Adamek, R., Smeekes, S., & Wilms, I. (2024). Local projection inference in high dimensions. Econometrics Journal, 27(3), 323-342. https://doi.org/10.1093/ectj/utae012