A Neutral network boosted overdispersed Poisson claims reserving model
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| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20200009900 | ||
| 003 | MAP | ||
| 005 | 20200326142041.0 | ||
| 008 | 200326e20200101bel|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20200006558$aGabrielli, Andrea | ||
| 245 | 1 | 2 | $aA Neutral network boosted overdispersed Poisson claims reserving model$cAndrea Gabrielli |
| 520 | $aWe 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. | ||
| 650 | 4 | $0MAPA20080592011$aModelos actuariales | |
| 650 | 4 | $0MAPA20090041721$aDistribución Poisson-Beta | |
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20080592042$aModelos matemáticos | |
| 773 | 0 | $wMAP20077000420$tAstin bulletin$dBelgium : ASTIN and AFIR Sections of the International Actuarial Association$x0515-0361$g01/01/2020 Volumen 50 Número 1 - enero 2020 , p. 25-60 |