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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/118279
Multivariate count data generalized linear models: Three approaches based on the Sarmanov Distribution [WP]
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Starting from the question: “What is the accident risk of an insured?”, this paper considers a multivariate approach by taking into account three types of accident risks and the possible dependence between them. Driven by a real data set, we propose three trivariate Sarmanov distributions with generalized linear models (GLMs) for marginals and incorporate various individual characteristics of the policyholders by means of explanatory variables. Since the data set was collected over a longer time period (10 years), we also added each individual’s exposure to risk. To estimate the parameters of the three Sarmanov distributions, we analyze a pseudo-maximumlikelihood method. Finally, the three models are compared numerically with the simpler trivariate Negative Binomial GLM.
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BOLANCÉ LOSILLA, Catalina i VERNIC, Raluca. Multivariate count data generalized linear models: Three approaches based on the Sarmanov Distribution [WP]. IREA – Working Papers. 2017. Vol. IR17/18. ISSN 1136-8365. [consulta: 7 de maig de 2026]. Disponible a: https://hdl.handle.net/2445/118279