A Neutral network boosted overdispersed Poisson claims reserving model
Título: A Neutral network boosted overdispersed Poisson claims reserving model / Andrea Gabrielli
Autor: Gabrielli, Andrea
Notas: Sumario: We present an actuarial claims reserving technique that takes into account both claim counts and claim amounts. Separate (overdispersed) Poisson models for the claim counts and the claim amounts are combined by a joint embedding into a neural network architecture. As starting point of the neural network calibration, we use exactly these two separate (overdispersed) Poisson models. Such a nested model can be interpreted as a boosting machine. It allows us for joint modeling and mutual learning of claim counts and claim amounts beyond the two individual (overdispersed) Poisson models.
Otras clasificaciones: 6
Derechos: In Copyright (InC): http://rightsstatements.org/vocab/InC/1.0/