Generalized Pareto Regression Trees for Extreme Claims Prediction

Generalized Pareto Regression Trees for Extreme Claims Prediction


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Tree-based methods are convenient and powerful machine learning tools that
can be seen as alternatives to classical regression and prediction models such
as generalized linear models, see for example. The most standard procedures
are designed to estimate the expectation of a random variable, that is, when it
comes to risk, a central scenario (or a best estimate using the Solvency II
terminology). In this work, we propose an extension of these tree methods to
the study of extreme events, which are of particular interest when it comes to
investigate the tail of the distribution and design reinsurance policies. We
propose a detailed description of our adaptation of decision trees and support
the methodology with new consistency results on these topics. We illustrate
the performance of the procedure on simulated data and on a cyberdata base.

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