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Working paper

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2017

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cc-by-nc-nd, (c) Bolancé Losilla et al., 2017
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/118279

Multivariate count data generalized linear models: Three approaches based on the Sarmanov Distribution [WP]

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Abstract

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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Variables (Matemàtica), Variables aleatòries, Teoria de distribucions (Anàlisi funcional), Teoria de l'estimació

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Variables (Mathematics), Random variables, Theory of distributions (Functional analysis), Estimation theory

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Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

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BOLANCÉ LOSILLA, Catalina and 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. [consulted: 7 of October of 2026]. Available at: https://hdl.handle.net/2445/118279

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