Kolloquium des ZeSt

geplante Vortragstermine (über zoom):

Dienstag, 27.04.2021, 12-13 Uhr - Vortrag über zoom

Rouven Michels
Universität Bielefeld

Titel folgt


Dienstag, 11.05.2021, 12-13 Uhr - Vortrag über zoom

Christoph Kiefer
Universität Bielefeld

Treatment Effects on Count Outcomes With Latent Covariates

The effects of a treatment on a count outcome can be assessed using a Poisson or negative binomial regression, where treatment effects are defined as the difference between the expected outcome under treatment and under control. These treatment effects can to date only be estimated if all covariates are manifest (observed) variables. However, some covariates are latent variables that are measured by multiple fallible indicators (e.g., depression). In such cases, it is important to control for measurement error of covariates in order to avoid attenuation bias and to get unbiased treatment effect estimates. In this talk, we present a new approach to compute average and conditional treatment effects in regression models with a logarithmic link function involving multiple latent and/or manifest covariates. Building on a multigroup SEM framework for count variables instead of the generalized linear model, count regression models with multiple latent and/or manifest covariates can be estimated. Then, average and conditional treatment effects are computed using analytical formulas based on moment-generating functions. We provide an illustrative example of our approach and evaluate a cognitive training in elderly people controlling for their (latent) pretest depression and locus of control. The model and effect estimation for the illustrative example are carried out with open-source software packages in R.

The talk will be held in English.


Dienstag, 25.05.2021, 12-13 Uhr - Vortrag über zoom

Prof. Dr. Göran Kauermann
Institut für Statistik der Ludwig-Maximilians-Universität München

Statistics in the COVID-19 pandemics – Challenges, Frustrations and Opportunities

The talk reports about the work of the COVID-19 Data Analysis Group (CODAG) at LMU Munich (https://www.covid19.statistik.uni-muenchen.de/index.html). The group started working on COVID-19 infection data as early as in March 2020, just when the pandemic stated and focussed on various aspects. This included the development of surveillance tools and prediction models. Nowcasting techniques were used to account for reporting delays and imputation techniques were developed to analyse time effects on the onset of the disease rather than on the testing date. We also looked at excess-mortality and the influence of mobility on disease spread. Besides methodological challenges our work was increasingly accompanied by frustration. Data quality, data accessibility and limited impact of our work on policy makers marked a turning point, when we started issuing a biweekly report. This found extended coverage in public media and even brought our colleagues to TV interviews. The talk illustrates some of our research and provides an aerial view on the pandemic from a statistician’s point of view.

The talk will be held in English.


Dienstag, 08.06.2021, 12-13 Uhr - Vortrag über zoom


Dienstag, 22.06.2021, 12-13 Uhr - Vortrag über zoom


Dienstag, 06.07.2021, 12-13 Uhr - Vortrag über zoom