arXiv subjects

Isabella Habereder

Publications and source records attributed to Isabella Habereder.

2 recordsLinked to original sources

arXiv2026

Structural Under-Representation of Women in News: Nonparametric Bayesian Mixtures Capture Time-Dependent Dynamics

The under-representation of women as sources cited in news media is one prominent representation of gender bias. Understanding where gender bias concentrates and how it evolves is essential for targeted mitigation. Because gender representation varies across topics, time, and reported-on regions, creating complex dependencies that are difficult to capture parametrically, we employ a nonparametric model to uncover latent cluster structures and temporal dynamics. We combine time-dependent Bayesian mixture modeling techniques with a Beta mixture kernel tailored to female quote shares, bounded between 0 and 1. Fitted on Canadian news articles from 2019 to 2024, the model reveals structural under-representation of women across all clusters, with news topic driving differences in female quote shares more strongly than the reported-on region. More than 85% of topic-region time series show no improvement toward gender parity over the observation period. Dynamic density estimation confirms that the aggregate distribution of female quote shares remains stable between 2019 and 2024. Our application demonstrates that advanced probabilistic models not only reproduce findings in gender bias research but also reveal latent dependencies and structural patterns that simpler approaches miss, encouraging future adoption of model-based frameworks for studying media bias.

stat.AP↗

arXiv2025

A Systematic Review of Spatio-Temporal Statistical Models: Theory, Structure, and Applications

Data with spatio-temporal attributes are prevalent across many research fields, and statistical models for analyzing spatio-temporal relationships are widely used. Existing reviews focus either on specific domains or model types, creating a gap in comprehensive, cross-disciplinary overviews. To address this, we conducted a systematic literature review following the PRISMA guidelines, searched two databases for the years 2021-2025, and identified 83 publications that met our criteria. We propose a classification scheme for spatio-temporal model structures and highlight their application in the most common fields: epidemiology, ecology, public health, economics, and criminology. Although tasks vary by domain, many models share similarities. We found that hierarchical models are the most frequently used, and most models incorporate additive components to account for spatio-temporal dependencies. The preferred model structures differ among fields of application. We also observe that research efforts are concentrated in only a few specific disciplines, despite the broader relevance of spatio-temporal data. Furthermore, we notice that reproducibility remains limited. Our review, therefore, not only offers inspiration for comparing model structures in an interdisciplinary manner but also highlights opportunities for greater transparency, accessibility, and cross-domain knowledge transfer.

stat.AP↗