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

When to Collect Data? A Sobol Index Strategy for Model-Based Experimental Design

Optimizing measurement timing in experimental studies is crucial for reducing animal sacrifices, especially when investigating systems characterized by ordinary differential equations (ODEs). Conventional sampling strategies frequently collect excessive data points without substantially improving parameter estimation accuracy. Our research introduces a new experimental design framework that analyzes the temporal evolution of variance-based sensitivities in ODE-driven processes. By integrating time-dependent Sobol indices with underlying system dynamics, this approach identifies the most informative sampling points, particularly during phases where parameter interactions are the primary contributors to system variance. Both theoretical analyses and computational experiments confirm that our strategically selected measurement times yield better parameter estimation outcomes compared to traditional protocols.

 

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

Prof. Dr. Christoph Kayser
Universität Bielefeld

Titel folgt

 

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

Paul Heimerl
Department of Economics and Business Economics der Aarhus University

Estimation of Latent Group Structures in Time-Varying Panel Data Models

We introduce a panel data model where coefficients vary both over time and the cross-section. Slope coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. The group structure is identified using a pairwise adaptive group fused-Lasso penalty. The trajectories of time-varying coefficients are estimated via polynomial spline functions. We derive the asymptotic distributions of the penalized and post-selection estimators and show their oracle efficiency. A simulation study demonstrates excellent finite sample properties. An application to the pace of microclimate change highlights the relevance of addressing cross-sectional heterogeneity and time-variance in empirical settings.

 

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