Výsledky vyhledávání - "Reynkens, Tom"

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Social network analytics for supervised fraud detection in insurance

Autor: Óskarsdóttir, María, Ahmed, Waqas, Antonio, Katrien, Baesens, Bart, Dendievel, Rémi, Donas, Tom, Reynkens, Tom

Insurance fraud occurs when policyholders file claims that are exaggerated or based on intentional damages. This contribution develops a fraud detection strategy by extracting insightful information from the social network of a claim. First, we const

Externí odkaz: http://arxiv.org/abs/2009.08313

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Sparse Regression with Multi-type Regularized Feature Modeling

Autor: Devriendt, Sander, Antonio, Katrien, Reynkens, Tom, Verbelen, Roel

Publikováno v: Insurance: Mathematics and Economics (2020)

Within the statistical and machine learning literature, regularization techniques are often used to construct sparse (predictive) models. Most regularization strategies only work for data where all predictors are treated identically, such as Lasso re

Externí odkaz: http://arxiv.org/abs/1810.03136

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Estimating the maximum possible earthquake magnitude using extreme value methodology: the Groningen case

Autor: Beirlant, Jan, Kijko, Andrzej, Reynkens, Tom, Einmahl, John H. J.

Publikováno v: Natural Hazards (2018)

The area-characteristic, maximum possible earthquake magnitude $T_M$ is required by the earthquake engineering community, disaster management agencies and the insurance industry. The Gutenberg-Richter law predicts that earthquake magnitudes $M$ follo

Externí odkaz: http://arxiv.org/abs/1709.07662

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Modelling Censored Losses Using Splicing: a Global Fit Strategy With Mixed Erlang and Extreme Value Distributions

Autor: Reynkens, Tom, Verbelen, Roel, Beirlant, Jan, Antonio, Katrien

Publikováno v: Insurance Math. Econom. 77 (2017) 65-77

In risk analysis, a global fit that appropriately captures the body and the tail of the distribution of losses is essential. Modelling the whole range of the losses using a standard distribution is usually very hard and often impossible due to the sp

Externí odkaz: http://arxiv.org/abs/1608.01566

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Fitting tails affected by truncation

Autor: Beirlant, Jan, Alves, Isabel Fraga, Reynkens, Tom

Publikováno v: Electron. J. Stat. 11 (2017) 2026-2065

In several applications, ultimately at the largest data, truncation effects can be observed when analysing tail characteristics of statistical distributions. In some cases truncation effects are forecasted through physical models such as the Gutenber

Externí odkaz: http://arxiv.org/abs/1606.02090

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