Dienstag, 15.04.2025, 12-13 Uhr in W9-109

Prof. Dr. Dietmar Bauer
Universität Bielefeld

Using Subspace Methods for the Estimation of Approximate Dynamic Factor  Models

For multivariate time series with a large number of variables classical vector autoregressive (VAR) models are not appropriate because they contain too many parameters. Alternatively in the literature in such situations factor models are used to reduce the dimensionality. Approximate dynamic factor models represent the high-dimensional time series as generated by a common factor part and idiosyncratic terms, where the common factors are latent. Estimating the dynamics of the common factors often is done using a VAR model for the principal components. The estimation of more flexible state space models via maximum likelihood methods is more complicated. Subspace methods are a numerically stable alternative that can be used in this respect. In this talk we show that the subspace methods provide a very robust and computationally simple means to obtain consistent estimators for the latent factor dynamics.

 

Dienstag, 29.04.2025, 12-13 Uhr in W9-109

Houda Yaqine
Universität Bielefeld

Titel folgt

 

Dienstag, 13.05.2025, 12-13 Uhr in W9-109

 

Dienstag, 27.05.2025, 12-13 Uhr in W9-109

Sophie Potts
Georg-August-Universität Göttingen

Titel folgt

 

Dienstag, 10.06.2025, 12-13 Uhr in W9-109

Prof. Dr. Göran Kauermann
Institut für Statistik der Ludwig-Maximilians- Universität München

More on Uncertainty in Machine Learning

The quantification of uncertainty is continuously gaining interest in the machine learning community. Tackling this question can be done with statistical tools, getting back to the foundation of statistics, namely how to cope with uncertainty and ignorance. The talk extends our previous work on this topic and presents some new results and insights. We discuss different sources of uncertainty and advocate an increased use of statistics and statistical methods in the machine learning world. To showcase our view we look at ambiguity in computer linguistics, we sketch the use of statistics in network weight reconstruction and focus on labelling ambiguity. While these examples are heterogeneous, the data centric view and hence its statistical foundation serves as common thread.

 

Dienstag, 24.06.2025, 12-13 Uhr in W9-109

 

Dienstag, 08.07.2025, 12-13 Uhr in W9-109