Dienstag, 21.04.2020, 12-13 Uhr - Raum: W9-109fällt aus
Dienstag, 05.05.2020,12-13 Uhr - Raum: W9-109fällt aus
Dienstag, 19.05.2020, 12-13 Uhr - Raum: W9-109fällt aus
Dienstag, 02.06.2020, 12-13 Uhr - Raum: W9-109
Prof. Dr. Göran Kauermann
Institut für Statistik der Ludwig-Maximilians-Universität München
Vortrag wird verschoben!
Aufgrund der aktuellen Situation greifen wir in diesem Semester auch auf aufgezeichnete Videovorträge zurück. Einige Vorträge sind passwortgeschützt. Bei Interesse am Vortrag, melden Sie sich bitte bei Dr. Nina Westerheide (email@example.com) oder direkt bei der bzw. dem Vortragenden, um das Passwort in Erfahrung zu bringen.
Prof. Dr. Roland Langrock
Universität Bielefeld, Videovortrag im 04.05.2020 an der University of Cape Town (UCT)
In sports, the concept of the "hot hand" refers to the idea that athletes may enter a state in which they experience exceptional success. For example, in basketball, players are commonly referred to as being "in the zone" or "on fire" when they hit several shots in a row. Although players, managers and fans strongly believe in its existence, the hot hand has been the subject of intense scientific debate over more than three decades, with some researchers attributing the alleged effect to a cognitive illusion. Here we investigate the hot hand hypothesis in professional darts as a near-ideal setting with minimal to no interaction between players. Considering almost one year of tournament data, corresponding to 167,492 dart throws in total, state-space models are used to investigate serial dependence in throwing performances. In these models, a latent state process serves as a proxy for a player's underlying ability, and autoregressive processes are used to model how this process evolves over time. The results show a strong but short-lived serial dependence in the latent state process, thus providing evidence for the existence of the hot hand.
Prof. Dr. Dietmar Bauer
Universität Bielefeld, Vortrag vom 13.12.2019 in der Lecture Series Data Science des Bielefeld Center for Data Science (BiCDaS)
Knowing the travel time for a given route is of importance for logistics applications, but also for individual travel, as is documented by the provision of predictions for example in Google maps. In both applications we do not only want to know the expected travel time but also the associated uncertainty as typically being late might incur a larger penalty than being too early. For predicting travel times a large range of different time series methods are applied using a very diverse landscape of data sources with associated strengths and problems. The methods used include a large number of time series analysis
The data lecture (Vortrag auf YouTube vom 27.10.2015)
Dienstag, 07.07.2020, 12-13 Uhr - Vortrag über zoom
Prof. Dr. Kevin Tierney
Exploiting Counterfactuals for Scalable Stochastic Optimization
We propose a new framework for decision making under uncertainty to overcome the main drawbacks of current technology: modeling complexity, scenario generation, and scaling limitations. We consider three NP-hard optimization problems: the Stochastic Knapsack Problem (SKP), the Stochastic Shortest Path Problem (SSPP), and the Resource Constrained Project Scheduling Problem (RCPSP) with uncertain job durations, all with recourse. We illustrate how an integration of constraint optimization and machine learning technology can overcome the main practical shortcomings of the current state of the art.
Dienstag, 14.07.2020, 12-13 Uhr -
Anne Scheunemann, M.Sc. und Theresa Schnettler, M.Sc.
Postponed, dissatisfied, and dropped out? A longitudinal analysis of the relationship between procrastination, study satisfaction, and intention to drop out
Study dropout is a multi-causal process, which is precede by the formation of an intention to drop out. Study satisfaction and dysfunctional study habits are considered as possible antecedents for forming an intention to drop out. Academic procrastination is an example for a dysfunctional study behaviour since academic tasks are postponed despite the opportunity to act and against one’s better knowledge. Empirical studies show bivariate relationships between procrastination, study satisfaction, and intention to drop out, but their relationships have not yet been considered together. Moreover, the empirical evidence is limited to cross-sectional designs. Thus, no direction of the relationships can be determined, nor is it taken into account that the intention to drop out can in turn have negative effects on study behaviour (i.e. procrastination) and study satisfaction. To address his research gap we conducted a longitudinal study with three measurement points over the course of one semester (N = 326), which was carried out as part of the BMBF-Project ProkRASt. The results of a latent cross-lagged panel model of the temporal relationships between procrastination, study satisfaction and the intention to drop out will be presented.