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http://hdl.handle.net/2445/118279
Title: | Multivariate count data generalized linear models: Three approaches based on the Sarmanov Distribution [WP] |
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: http://www.ub.edu/irea/working_papers/2017/201718.pdf |
It is part of: | IREA – Working Papers, 2017, IR17/18 |
URI: | http://hdl.handle.net/2445/118279 |
ISSN: | 1136-8365 |
Appears in Collections: | Documents de treball (Institut de Recerca en Economia Aplicada Regional i Pública (IREA)) |
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IR17-018_Bolance.pdf | 856.22 kB | Adobe PDF | View/Open |
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