Computationally-intensive methods for the robust analysis of non-standard data (CRoNoS)

Real data sets from a wide variety of fields violate the idealized assumptions inherent in standard statistical theory. Robust data analysis methodology aims to mitigate the impact of such violations.

New models, methods and efficient, numerically stable, and well-conditioned robust strategies are essential to improve knowledge extraction from non-perfect and non-standard datasets.

The Action will provide European scientists with cutting-edge data analysis tools, which will be suitably disseminated by disparate means such as training schools, conferences and publications. Improved decision-making tools for preventing-mitigating policies will be derived.

Beit Berl College Faculty: Dr. Yossi Arzouan 2015-2019.


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