ArticleStatistics and computing2026
Model-based clustering of time-dependent observations with common structural changes.
Article in Statistics and computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
We propose a novel model-based clustering approach for samples of time series. We assume as a unique commonality that two observations belong to the same group if structural changes in their behaviors happen at the same time. We resort to a latent representation of structural changes in each time series, based on random orders, to induce ties among different observations. Such an approach results in a general modeling strategy and can be combined with many time-dependent models already known in the literature. Our studies have been motivated by an epidemiological problem. Specifically, we want to provide clusters of different countries of the European Union where two countries belong to the same cluster if the spreading processes of the COVID-19 virus show structural changes at the same time. Supplementary Information: The online version contains supplementary material available at 10.1007/s11222-025-10756-x.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.