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Center for Empirical Studies (CEST)

The Center for Empirical Studies is a research initiative, linking empirical and methodological research groups from several different faculties (among others the Workgroup Financial Mathematics). Methodological challenges include modeling unobservable heterogeneity or measuring latent traits, that recur in similar form in, for example, economic, sociological and psychological tasks. The aim of the Center of Empirical Studies is thus to enhance the explanatory power of empirical studies by means of new methodological developments. The initiative is organized in three interacting areas:

Statistical Learning, Data Mining & Knowledge Discovery

The broad goal of data mining and knowledge discovery is the investigation and discovery of specific associations from large scale databases by means of data processing approaches and algorithms. The methods are based on machine learning and artificial intelligence approaches, some of which are advancements of classical techniques from multivariate statistics. This expanding field of research has developed from the interaction of computer scientists and statisticians and is predestined for cooperations across these fields. Important application areas, that are represented in the projects, cover the modelling of risks and decisions.

Measurement and Evaluation

The broad goal of data mining and knowledge discovery is the investigation and discovery of specific associations from large scale databases by means of data processing approaches and algorithms. The methods are based on machine learning and artificial intelligence approaches, some of which are advancements of classical techniques from multivariate statistics. This expanding field of research has developed from the interaction of computer scientists and statisticians and is predestined for cooperations across these fields. Important application areas, that are represented in the projects, cover the modelling of risks and decisions.

Dynamic Modeling

A special methodological challenge is the modeling of time-dependent data. Here the main focus is on understanding the dynamics of time-dependent processes and the forecasts depending upon them. Application examples range from dynamic modeling of financial- and real-economic interactions to the circadian rhythms of humans.

For more information on the Center for Empirical Studies please visit their website.