arXiv subjects

Marléne Baumeister

Publications and source records attributed to Marléne Baumeister.

4 recordsLinked to original sources

arXiv2026

Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods

Statistical practice does not automatically follow methodological innovation. Regularization methods, widely advocated to reduce overfitting and stabilize inference, are readily available in modern software, but are not consistently used by data analysts. We investigate this implementation gap in a large-scale empirical study of trust in, and acceptance of, regularization techniques, based on $N = 606$ data analysts. Drawing on measurement frameworks from technology acceptance research, we survey practitioners and embed a randomized experiment to test whether written recommendation of regularization methods increases trust or intended use. We find no evidence of such an effect. Instead, adoption intentions are strongly associated with analysts' perceptions of ease of implementation and practical benefit, such as improved bias control or interpretability. Perceived social norms also emerge as a central driver. These results indicate that uptake of statistical methodology depends less on formal recommendations than on usability, perceived utility, and community practice.

stat.OT↗

arXiv2025

Multivariate and Multiple Contrast Testing in General Covariate-adjusted Factorial Designs

Evaluating intervention effects on multiple outcomes is a central research goal in a wide range of quantitative sciences. It is thereby common to compare interventions among each other and with a control across several, potentially highly correlated, outcome variables. In this context, researchers are interested in identifying effects at both, the global level (across all outcome variables) and the local level (for specific variables). At the same time, potential confounding must be accounted for. This leads to the need for powerful multiple contrast testing procedures (MCTPs) capable of handling multivariate outcomes and covariates. Given this background, we propose an extension of MCTPs within a semiparametric MANCOVA framework that allows applicability beyond multivariate normality, homoscedasticity, or non-singular covariance structures. We illustrate our approach by analysing multivariate psychological intervention data, evaluating joint physiological and psychological constructs such as heart rate variability.

stat.ME↗

arXiv2025

Early and Late Buzzards: Comparing Different Approaches for Quantile-based Multiple Testing in Heavy-Tailed Wildlife Research Data

In medical, ecological and psychological research, there is a need for methods to handle multiple testing, for example to consider group comparisons with more than two groups. Typical approaches that deal with multiple testing are mean or variance based which can be less effective in the context of heavy-tailed and skewed data. Here, the median is the preferred measure of location and the interquartile range (IQR) is an adequate alternative to the variance. Therefore, it may be fruitful to formulate research questions of interest in terms of the median or the IQR. For this reason, we compare different inference approaches for two-sided and non-inferiority hypotheses formulated in terms of medians or IQRs in an extensive simulation study. We consider multiple contrast testing procedures combined with a bootstrap method as well as testing procedures with Bonferroni correction. As an example of a multiple testing problem based on heavy-tailed data we analyse an ecological trait variation in early and late breeding in a medium-sized bird of prey.

stat.ME↗

arXiv2022

Quantile-based MANOVA: A new tool for inferring multivariate data in factorial designs

Multivariate analysis-of-variance (MANOVA) is a well established tool to examine multivariate endpoints. While classical approaches depend on restrictive assumptions like normality and homogeneity, there is a recent trend to more general and flexible proce dures. In this paper, we proceed on this path, but do not follow the typical mean-focused perspective. Instead we consider general quantiles, in particular the median, for a more robust multivariate analysis. The resulting methodology is applicable for all kind of factorial designs and shown to be asymptotically valid. Our theoretical results are complemented by an extensive simulation study for small and moderate sample sizes. An illustrative data analysis is also presented.

math.ST↗