Statistical modeling of emerging risks with missing and censored data

Navn på bevillingshaver

Martin Bladt


Associate Professor


University of Copenhagen


DKK 4,755,674




Semper Ardens: Accelerate


The project addresses statistical modeling of emerging risks, especially catastrophic events, with a focus on missing and censored data. It aims to bridge the knowledge gap in extreme value theory when observations are incomplete through mathematical statistics and machine-learning methods, ensuring accurate risk estimation.


Emerging risks, like catastrophic events, demand precise risk estimation. Current methods fall short when it comes to dealing with extreme events that have missing or censored data. This research aims to leverage mathematical expertise in order to develop unified statistical and computational tools that can provide more accurate risk assessment in such scenarios.


The project will develop a unified mathematical framework for extreme events with censored data, create efficient computational methods, and address challenges posed by missing data. These methods will then be applied to complex and large datasets associated with emerging risks, ensuring their practical relevance and effectiveness.

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