Dienstag, 22.10.2024, 12-13 Uhr in W9-109

apl. Prof. Dr. Odile Sauzet
Universität Bielefeld

Exploring the effects of dichotomisation in survival analysis and suggestion of a distributional approach

The limitations resulting from the  dichtomisation of continuous outcome have been extensively described. But the need to present results based on binary outcomes in particular in health science remains. Alternatives based on the distribution of the continuous outcome have been proposed. Here we explore the possibilities of using a distributional approach in the context of time-to-event analysis when the event is the results of the dichotomisation of a continuous outcome. For this we propose in a first step a distributional version of the Kaplan-Meier estimate of the survival function.

 

Dienstag, 05.11.2024, 12-13 Uhr in W9-109

Jan-Ole Koslik
Universität Bielefeld

Efficient smoothness selection for nonparametric Markov-switching models via quasi restricted maximum likelihood estimation

Markov-switching models are powerful tools that allow capturing complex patterns from time series data driven by latent states. Recent work has highlighted the benefits of estimating components of these models nonparametrically, enhancing their flexibility and reducing biases, which in turn can improve state decoding, forecasting, and overall inference. Formulating such models using penalised splines is straightforward, but practically feasible methods for a data-driven smoothness selection in these models are still lacking. Traditional techniques, such as cross-validation and information criteria-based selection suffer from major drawbacks, most importantly their reliance on computationally expensive grid search methods, hampering practical usability for Markov-switching models. Michelot (2022) suggested treating spline coefficients as random effects with a multivariate normal distribution and using the R package TMB (Kristensen et al., 2015) for marginal likelihood maximisation. While this method avoids grid search and typically results in adequate smoothness selection, it entails a nested optimisation problem, thus being computationally demanding. We propose to exploit the simple structure of penalised splines treated as random effects, thereby greatly reducing the computational burden while potentially improving fixed effects parameter estimation accuracy. The proposed method offers a reliable and efficient mechanism for smoothness selection, rendering the estimation of Markov-switching models involving penalised splines feasible for complex data structures.

 

Dienstag, 19.11.2024, 12-13 Uhr in W9-109

Nayeli Gast Zepeda
Universität Bielefeld

Titel folgt

 

Dienstag, 03.12.2024, 12-13 Uhr in W9-109

Aktuelle Forschungsthemen im ZeSt
 

Dienstag, 17.12.2024, 12-13 Uhr in W9-109

Dr. Christoph Kiefer
Universität Bielefeld

Definition and Identification of Causal Ratio Effects

 

Dienstag, 14.01.2025, 12-13 Uhr in W9-109

Johannes Brachem
Georg-August-University Göttingen

Titel folgt

 

Dienstag, 28.01.2025, 12-13 Uhr in W9-109

Prof. Dr. Jan Gertheiss
Helmut-Schmidt-Universität

Titel folgt