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Title: Multivariate count data generalized linear models: Three approaches based on the Sarmanov Distribution
Author: Bolancé Losilla, Catalina
Vernic, Raluca
Keywords: Variables (Matemàtica)
Variables aleatòries
Teoria de distribucions (Anàlisi funcional)
Teoria de l'estimació
Variables (Mathematics)
Random variables
Theory of distributions (Functional analysis)
Estimation theory
Issue Date: 2017
Publisher: Universitat de Barcelona. Facultat d'Economia i Empresa
Series/Report no: [WP E-IR17/18]
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.
Note: Reproducció del document publicat a:
It is part of: IREA – Working Papers, 2017, IR17/18
ISSN: 1136-8365
Appears in Collections:Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA))

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