Statistical Inference Methods for Neuroeconomics

• Applicant: Professor Dr. Thorsten Dickhaus
• Subject Area: Statistics and Econometrics
• Term: from 2013 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 239049500

The proposed project contributes to fundamental research in behavioral genetics and neuroeconomics by developing refined statistical inference methods for data generated in these fields. In particular, techniques for multiple hypotheses testing will be refined, adapted and newly worked out. Multiple tests are needed in behavioral genetics in order to analyze associations between many genetic markers and behavioral phenotypes simultaneously. In neuroeconomics, high-dimensional and spatially clustered functional magnetic resonance imaging time series have to be analyzed with multiple testing techniques. We will apply the methods resulting from the research in this project to risk preference and genetics data that we have compiled in prior work. Furthermore, our methodological contributions will be applicable in many other fields, too: High-dimensional categorical data are also prevalent, for example, in genetic epidemiology and high-dimensional hierarchical data structures occur for instance in spatial statistics or in the context of the analysis of variance with many groups.

• DFG Programme: Research Grants