Kolloquium des ZeSt

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 (nwesterheide@uni-bielefeld.de) oder direkt bei der bzw. dem Vortragenden, um das Passwort bzw. den zoom-Link in Erfahrung zu bringen.

 

Videovorträge

Prof. Dr. Roland Langrock
Universität Bielefeld, Vortrag am 03.07.2017 an der University of Groningen (RUG)

Nonparametric inference in hidden Markov and related models

Hidden Markov models (HMMs) are versatile models for univariate and multivariate time series. In their basic setup, HMMs comprise two stochastic processes, only one of which is directly observed. The observed (state-dependent) process is in some way driven by the unobserved, serially correlated (state) process. In these modelling classes even parametric inference can be computationally challenging, since the likelihood involves the consideration of all possible state sequences that might have given rise to the observations. Nonparametric techniques for flexible modelling, which are nowadays routinely and very successfully used for example in regression or in density estimation, are only beginning to be applied also in HMM-type modelling classes. In this talk, after giving a general introduction to statistical inference for HMMs, I will demonstrate how the powerful HMM machinery can be combined with the general advantages of penalized splines to allow for flexible nonparametric inference in general-purpose HMM-type classes of models, including Markov-switching regression models. The focus of the presentation will lie on practical aspects of nonparametric modelling in these frameworks, with the methods being illustrated in ecological and economic real data examples.

The talk will be held in English.

 

 

weitere geplante Vortragstermine (über zoom):

Dienstag, 10.11.2020, 12-13 Uhr - Vortrag über zoom

David Winkelmann, M.Sc.
Universität Bielefeld

Can we beat the market? - Long-term betting strategies in European football

Research on sports betting often attempts to identify biased evaluation by bookmakers, opening opportunities for profitable strategies to bettors. Previous studies have provided evidence for the existence of such inefficiencies. Since most studies cover only a few seasons, the question of whether market inefficiencies persist over time remains unanswered. We analyse the big five leagues in European association football for 14 seasons to detect the occurrence and duration of market inefficiencies. While our results suggest that most biases do not persist for a long time, we still uncover profitable betting strategies throughout the full observation period.

The talk will be held in English.

 

Dienstag, 24.11.2020, 12-13 Uhr - Vortrag über zoom

Lennart Oelschläger, M.Sc.
Universität Bielefeld

Approximating mixing distributions in probit models via a Bayesian approach

The multinomial probit model is one of the most widely-used statistical models to explain the choices that individuals make among a discrete set of alternatives, which is of central interest in many scientific areas, for example in transportation economics and marketing. In many such choice scenarios it is reasonable to assume, that the preferences of the decision makers are non-homogeneous, which can be modelled by imposing mixing distributions on the coefficients. Currently, the literature does not provide much guidance for the specification of the mixing distributions apart from trial and error procedures using several standard parametric distributions. To fill this gap, this talk presents a new approach that combines Bayes estimation and semi-parametric methods: A Bayesian framework is used for estimating a latent class mixed multinomial probit model where the number of latent classes is updated within the algorithm on a weight-based strategy. Based on simulation results, we show that the approach is capable of approximating the underlying mixing distributions, reproducing the latent classes and thereby guiding the specification of mixing distributions in empirical applications.

The talk will be held in English.

 

Dienstag, 08.12.2020, 12-13 Uhr - Vortrag über zoom

Dr. Turid Frahnow und Dr. Peter Pütz
Universität Bielefeld

KoCo19: A Corona antibody study in Munich

Starting in the end of 2019, a recently discovered coronavirus (SARS-CoV-2) spread all over the world causing a severe respiratory syndrome with sometimes fatal consequences. Due to its impact on health, economy and daily life, validated information about the virus, the disease (COVID-19) and predictions regarding infection rates are of great interest. In Munich, a population-based serological study was conducted aiming for a better understanding of the pandemic. In this talk, we (on behalf of the whole study team) want to share first results with the audience about SARS-CoV-2 seroprevalence and epidemiological insights into the pandemic.

The talk will be held in English.

 

Dienstag, 12.01.2021, 12-13 Uhr - Vortrag über zoom

Anja Rappl, M.Sc.
Friedrich-Alexander-Universität Erlangen-Nürnberg

Joint Models in Mean Regression and Beyond

Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years, as they are a helpful technique to approach a common data structure in biostatistics where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent a potential bias introduced. Commonly, joint models are estimated in likelihood based expectation maximization or Bayesian approaches and are focused on the mean trajectory of the longitudinal outcome. Those frameworks lack of straight forward variable selection and do not immediately work for high-dimensional data situations. Gradient boosting is a method from the field of statistical learning which leads to automated variable selection and shrinkage. This talk introduces the boosting framework for joint models, which for the first time allows to estimate joint models in high-dimensional data situations. In addition to the commonly known variable selection feature, an extension which allows for allocation of the variables to the correct part of the model will be presented. Furthermore a Bayesian approach for extensions towards modelling different features beyond the mean of the longitudinal part of the model will be discussed.

The talk will be held in English.

 

Dienstag, 26.01.2021, 12-13 Uhr - Vortrag über zoom

Sina Mews, M.Sc.
Universität Bielefeld

Titel folgt

The talk will be held in English.

 

Dienstag, 09.02.2021, 12-13 Uhr - Vortrag über zoom

Prof. Dr. Axel Mayer
Universität Bielefeld

Titel folgt

The talk will be held in English.