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Vortragende Person/Vortragende Personen:
Prof. Dr. Philipp Otto

My focus will be on my research's methodological and empirical contributions. Regarding the first point, I will give a short overview of our spatial autoregressive conditional heteroscedasticity models, statistical monitoring of AI applications, as well as our works on regularised high-dimensional estimation of spatial dependence structures. Then, I will shift the main focus to the results of two case studies, thereby emphasising the empirical contributions -- one about the morphological evolution of coastal profiles and one about the usage patterns of a bike-sharing system. Whereas the first data set is highly irregular and often has incomplete measurements (see Figure), the second data set has a very detailed but regular temporal resolution. For both cases, we applied a functional model, which accounts for latent spatial and temporal effects, in combination with a spatial subsampling / bootstrap approach. I will show how this model can be used in two completely different situations and how we made the procedure scalable while accounting for the full spatial and temporal dependence.