2026-04-14T04:56:27Zhttps://recercat.cat/oai/requestoai:recercat.cat:2445/1182792025-12-04T22:59:13Zcom_2072_1057col_2072_478911col_2072_478917
RECERCAT
author
Bolancé Losilla, Catalina
author
Vernic, Raluca
2017-11-29T15:47:23Z2017-11-29T15:47:23Z20172017-11-29T15:47:23Z
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.
cc-by-nc-nd, (c) Bolancé Losilla et al., 2017 http://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess
Variables (Matemàtica)
Variables aleatòries
Teoria de distribucions (Anàlisi funcional)
Teoria de l'estimació
Variables (Mathematics)
Random variables
Theory of distributions (Functional analysis)
Estimation theory
Multivariate count data generalized linear models: Three approaches based on the Sarmanov Distribution [WP]
info:eu-repo/semantics/workingPaper