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The RobustStats project develops new statistical methods to enhance the reliability of conclusions drawn from large-scale data. It focuses on challenges like transfer learning, handling missing data, and model robustness to ensure accurate statistical inference in complex data environments.
Modern technology allows large-scale data to be collected in many new forms, and their underlying generating mechanisms can be extremely complex.
In fact, an interesting (and perhaps initially surprising) feature of large-scale data is that it is often much harder to feel confident that one has identified a plausible statistical model.
This is largely because there are so many forms of model violation and both visual and more formal statistical checks can become infeasible.
It is therefore vi…
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