Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.16914v1</id>\n    <title>A statistical perspective on transformers for small longitudinal cohort data</title>\n    <updated>2026-02-18T22:03:59Z</updated>\n    <link href='https://arxiv.org/abs/2602.16914v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.16914v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Modeling of longitudinal cohort data typically involves complex temporal dependencies between multiple variables. There, the transformer architecture, which has been highly successful in language and vision applications, allows us to account for the fact that the most recently observed time points in an individual's history may not always be the most important for the immediate future. This is achieved by assigning attention weights to observations of an individual based on a transformation of their values. One reason why these ideas have not yet been fully leveraged for longitudinal cohort data is that typically, large datasets are required. Therefore, we present a simplified transformer architecture that retains the core attention mechanism while reducing the number of parameters to be estimated, to be more suitable for small datasets with few time points. Guided by a statistical perspective on transformers, we use an autoregressive model as a starting point and incorporate attention as a kernel-based operation with temporal decay, where aggregation of multiple transformer heads, i.e. different candidate weighting schemes, is expressed as accumulating evidence on different types of underlying characteristics of individuals. This also enables a permutation-based statistical testing procedure for identifying contextual patterns. In a simulation study, the approach is shown to recover contextual dependencies even with a small number of individuals and time points. In an application to data from a resilience study, we identify temporal patterns in the dynamics of stress and mental health. This indicates that properly adapted transformers can not only achieve competitive predictive performance, but also uncover complex context dependencies in small data settings.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='stat.ME'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='stat.ML'/>\n    <published>2026-02-18T22:03:59Z</published>\n    <arxiv:primary_category term='stat.ME'/>\n    <author>\n      <name>Kiana Farhadyar</name>\n    </author>\n    <author>\n      <name>Maren Hackenberg</name>\n    </author>\n    <author>\n      <name>Kira Ahrens</name>\n    </author>\n    <author>\n      <name>Charlotte Schenk</name>\n    </author>\n    <author>\n      <name>Bianca Kollmann</name>\n    </author>\n    <author>\n      <name>Oliver Tüscher</name>\n    </author>\n    <author>\n      <name>Klaus Lieb</name>\n    </author>\n    <author>\n      <name>Michael M. Plichta</name>\n    </author>\n    <author>\n      <name>Andreas Reif</name>\n    </author>\n    <author>\n      <name>Raffael Kalisch</name>\n    </author>\n    <author>\n      <name>Martin Wolkewitz</name>\n    </author>\n    <author>\n      <name>Moritz Hess</name>\n    </author>\n    <author>\n      <name>Harald Binder</name>\n    </author>\n  </entry>"
}