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Statistical results of empirical studies in the behavioral and social sciences have repeatedly been shown to be reported in a suboptimal way. This PhD project seeks to understand why applied
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PhD on Motives for Statistical Reporting Practices (1.0 FTE) (V24.0329) « Back to the overview Job description Statistical results of empirical studies in the behavioral and social sciences have
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) population trajectories and state welfare provision data, available as reports and regional statistics; (meso-level) collective coping strategies through social organisations, collected through their archives
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) population trajectories and state welfare provision data, available as reports and regional statistics; (meso-level) collective coping strategies through social organisations, collected through their archives
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) population trajectories and state welfare provision data, available as reports and regional statistics; (meso-level) collective coping strategies through social organisations, collected through their archives
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models available. Project description Nonparametric statistical learning provides a flexible and data-driven approach to choice modeling. Recent advancements have improved its economic interpretability and
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data, available as reports and regional statistics; (meso-level) collective coping strategies through social organisations, collected through their archives and witness seminars, and (micro-level
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data, available as reports and regional statistics; (meso-level) collective coping strategies through social organisations, collected through their archives and witness seminars, and (micro-level
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position in the field of Machine learning meets choice models available. Project description Nonparametric statistical learning provides a flexible and data-driven approach to choice modeling. Recent
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mobile robotics. Towards agile sensing and inference, a series of original studies will be conducted based on uncertainty-aware kino-dynamic modeling and statistical machine learning. We put equal emphasis