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

Penalizing Infeasibility in Neural Combinatorial Optimization: An Experimental Study on Vehicle Routing Problems

Neural Combinatorial Optimization (NCO) methods have shown promise for vehicle routing problems (VRPs), with advances in problem scale and architectural improvements. However, these methods have primarily been tested on problems where feasible solutions are easily found. While previous work assumed neural networks could learn to respect constraints through penalty terms, we demonstrate experimentally that such penalty schemes fail to ensure solution feasibility, limiting the applicability of current NCO approaches to many real-world routing problems.

 

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

Aktuelle Forschungsthemen im ZeSt

Vorstellung verschiedener Forschungsportfolios mit besonderem Augenmerk auf potentielle Kooperationen und Masterarbeitsthemen

 

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

Dr. Christoph Kiefer
Universität Bielefeld

Definition and Identification of Causal Ratio Effects

In cases in which the outcome variable is binary (e.g., success/no success) or a count variable (e.g, number of depressive symptoms), the effect of a treatment or intervention is often expressed as ratio (e.g., risk ratio, odds ratio). While it is relatively straightforward to estimate some kind of ratio effect based on a logistic regression or Poisson regression, it is a non-trivial question whether ratio effect measures should be considered and if yes, how they can be interpreted and which assumptions need to be fulfilled in order for them to have a causal interpretation. For example, it is somewhat counter-intuitive in the context of ratio effects that an effect measure based on group averages does not necessarily resemble an average over individual effect measures, not even in randomized controlled trials. This phenomenon is known as (non-)collapsibility and has received quite a lot of attention in the biostatistics and epidemiology literature. In this talk, we use the stochastic theory of causal effects for defining different types of ratio effects and for clarifying the necessary assumptions for their identification. We briefly introduce the core aspects of the stochastic theory of causal effects before showing how to define ratio effects either as individual ratio effects or as average ratio effects. The different types of effects require different causality assumptions and have a different meaning, which only becomes clear when building on theories of causal effects.

 

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

Johannes Brachem
Georg-August-University Göttingen

Bayesian Penalized Transformation Models for Structured Additive Regression on the Location and Scale of Arbitrary Distributions

Penalized transformation models (PTMs) are a novel form of distribution-free location-scale regression. In PTMs, the shape of the response’s conditional distribution is estimated directly from the data, and structured additive predictors are placed on its location and scale. The core of the model is a monotonically increasing transformation function that relates the response distribution to a reference distribution. The transformation function is equipped with a smoothness prior that regularizes how much the estimated distribution diverges from the reference distribution. These models can be seen as a bridge between conditional transformation models and generalized additive models for location, scale and shape. Markov chain Monte Carlo inference for PTMs can be conducted with the No-U-Turn sampler and offers straightforward uncertainty quantification for the conditional distribution as well as for the covariate effects.

 

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

Prof. Dr. Jan Gertheiss
Helmut-Schmidt-Universität

Covariate-adjusted system outputs for structural health monitoring

Structural Health Monitoring (SHM) utilizes sensor technology to identify and detect potential damage to critical infrastructure like bridges. However, external factors such as temperature can affect the bridge’s behavior and the sensor measurements. The talk will discuss different data-driven methods to account for these confounding effects on sensor data or other system outputs, such as natural frequencies. The focus will be on a function-on-function regression framework for (nonlinear) modeling of the system outputs and adjusting for covariate-induced variation. This approach is particularly suited for long-term monitoring when several months or years of training data are available. It combines highly flexible yet interpretable semi-parametric modeling with functional principal component analysis. Furthermore, damage-sensitive features are readily accessible and can be monitored using state-of-the-art control charts.