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  • Geography, social context, time, and cultural mindset are four (out of many) cornerstones of human interaction. When building statistical models, their consideration is vital: They all cause dependency between individual observations, violating assumptions of independence and exchangeability. While this can be problematic and inhibit the unbiased inference of parameters, it can also be a fruitful source of insights and enhance prediction performance. One class of models that serves to manage or profit from the presence of dependence is the class of latent variable models. This class of models assumes that the presence of non-explicit, unobserved causes of continuous or discrete nature can explain the observed correlations. Latent variable models explicitly take account of dependency, for example, by modeling an unobserved local source of pollution as a continuous spatial variable. Through their widespread use for information fitering, link prediction, and statistical inference, latent variable models have developed an essential impact on our daily life and the way we consume information. The four articles in this thesis shed light on assumptions, usage, and potential drawbacks of latent variable models in various contexts that involve geographic and interaction data. We model unobserved sources of pollution in geophysical data, explore individual taste and mindsets in cross-cultural contexts, and predict the evolution of social relationships in software development projects. This combination of various perspectives contributes to the interdisciplinary exchange of methodological knowledge on the modeling of dependent data. (xsd:string)
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  • https://nbn-resolving.org/urn:nbn:de:bvb:739-opus4-7947. (EVS) (xsd:string)
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  • Geographic and Social Space in Latent Factor Models - Four Essays (xsd:string)
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