Detection of interacting variables for generalized linear models via neural networks
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<title>Detection of interacting variables for generalized linear models via neural networks</title>
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<namePart>Havrylenko, Yevhen </namePart>
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<namePart>Heger, Julia </namePart>
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<abstract displayLabel="Summary">In 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</abstract>
<note type="statement of responsibility">Yevhen Havrylenko & Julia Heger</note>
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<topic>Mercado de seguros</topic>
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<topic>Empresas de seguros</topic>
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<topic>Ciencias Actuariales y Financieras</topic>
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<url displayLabel="electronic resource" usage="primary display">https://link.springer.com/article/10.1007/s13385-023-00362-4</url>
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<title>European Actuarial Journal</title>
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<publisher>Cham, Switzerland : Springer Nature Switzerland AG, 2021-2022</publisher>
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<identifier type="local">MAP20220007085</identifier>
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<text>15/08/2024 Volumen 14 - Número 2 - agosto 2024 , p. 551-580</text>
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