Detection of interacting variables for generalized linear models via neural networks
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| Tag | 1 | 2 | Value |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20240013523 | ||
| 003 | MAP | ||
| 005 | 20240830121716.0 | ||
| 008 | 240830e20240815che|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a219 | ||
| 100 | 1 | $0MAPA20240021047$aHavrylenko, Yevhen | |
| 245 | 1 | 0 | $aDetection of interacting variables for generalized linear models via neural networks$cYevhen Havrylenko & Julia Heger |
| 520 | $aIn this paper, we propose a methodology for the detection of the next-best interaction that is missing in a benchmark GLM. We aim at improving an arbitrary but fixed existing benchmark GLM instead of creating a new GLM from scratch. Building a new GLM may necessitate drastic changes in the tariff of the MTPL insurance. Large changes in tariffs are not desired by insurance companies for their existing business lines. Instead, GLMs need to be improved gradually | ||
| 650 | 4 | $0MAPA20080586294$aMercado de seguros | |
| 650 | 4 | $0MAPA20080590567$aEmpresas de seguros | |
| 650 | 4 | $0MAPA20080627904$aCiencias Actuariales y Financieras | |
| 700 | 1 | $0MAPA20240021054$aHeger, Julia | |
| 773 | 0 | $wMAP20220007085$g15/08/2024 Volumen 14 - Número 2 - agosto 2024 , p. 551-580$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 | |
| 856 | $uhttps://link.springer.com/article/10.1007/s13385-023-00362-4 |