Dienstag, 20.10.2026, 12-13 Uhr in W9-109
Dr. Sebastian Büscher
Universität Bielefeld
Titel folgt
Dienstag, 03.11.2026, 12-13 Uhr in W9-109
PD Dr. Tobias Hepp
Friedrich-Alexander-Universität Erlangen-Nürnberg
Titel folgt
Dienstag, 17.11.2026, 12-13 Uhr in W9-109
Dr. Marléne Baumeister
TU Dortmund
Inference for Incomplete Paired Functional Data
Functional data analysis (FDA) has become increasingly popular in medical biometry and statistics. It is often appropriate to model observations by smooth curves or functions for example in the situation of observations that are sampled quite dense over time or space or in case of high-dimensional repeated measurements as FDA methods allow a flexible modelling. Furthermore, they do not assume a certain correlation structure between sampled cases or time points nor equally spaced time points. Despite many methodological developments in the last few years, there are still methodologically unsolved problems. One of them is missingness in context of functional data. That is why we consider testing equality of mean functions for paired functional data, where one entire function is missing. To use all available information from the paired and unpaired observations, we propose globalized test statistics that are based on a weighted sum of point-wise paired- and Welch--type statistics. By the use of empirical process theory, we derive the asymptotic limit distributions of the resulting test statistics under the null hypothesis and show consistency against fixed alternatives. As they are non-pivotal and contain complex dependencies from the paired and incomplete observations, a permutation and parametric bootstrap procedures is proposed to compute critical values. The work includes a theoretical investigation of the asymptotic behaviour of the two resampling approaches a permutation procedure and a parametric bootstrap, and state regularity conditions under which the corresponding resampling tests are valid. Simulation studies show that the proposed methods provide accurate type I error control and competitive power for most settings under study. The work is part of the WEAVE NCN-DFG Project OUTCLASS: Powerful Inference for Functional Data in Complex Factorial Designs by the principal investigators Prof. Łukasz Smaga from Adam Mickiewicz University in Poznań and Prof. Markus Pauly from TU Dortmund University.
Dienstag, 01.12.2026, 12-13 Uhr in W9-109
Aktuelle Forschungsbereiche des ZeSt
Dienstag, 15.12.2026, 12-13 Uhr in W9-109
Dienstag, 12.01.2026, 12-13 Uhr in W9-109
Dienstag, 26.01.2026, 12-13 Uhr in W9-109