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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: Models lineals (Estadística)
Anàlisi multivariable
Teoria de distribucions (Anàlisi funcional)
Teoria de l'estimació
Linear models (Statistics)
Multivariate analysis
Theory of distributions (Functional analysis)
Estimation theory
Issue Date: Mar-2019
Publisher: Elsevier B.V.
Abstract: Starting from the question: What is the accident risk of an insured individual?, we consider that the customer has contracted policies in different insurance lines: motor and home. Three models based on the multivariate Sarmanov distribution are analyzed. Driven by a real data set that takes into account three types of accident risks, two for motor and one for home, three trivariate Sarmanov distributions with generalized linear models (GLMs) for marginals are considered and fitted to the data. To estimate the parameters of these three models, we discuss a method for approaching the maximum likelihood (ML) estimators. Finally, the three models are compared numerically with the simpler trivariate Negative Binomial GLM and with elliptical copula based models.
Note: Versió postprint del document publicat a:
It is part of: Insurance Mathematics and Economics, 2019, vol. 85, p. 89-103
Related resource:
ISSN: 0167-6687
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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