Please use this identifier to cite or link to this item:
http://hdl.handle.net/2445/164643
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: https://doi.org/10.1016/j.insmatheco.2019.01.001 |
It is part of: | Insurance Mathematics and Economics, 2019, vol. 85, p. 89-103 |
URI: | http://hdl.handle.net/2445/164643 |
Related resource: | https://doi.org/10.1016/j.insmatheco.2019.01.001 |
ISSN: | 0167-6687 |
Appears in Collections: | Articles publicats en revistes (Econometria, Estadística i Economia Aplicada) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
684374.pdf | 104.44 kB | Adobe PDF | View/Open |
This item is licensed under a
Creative Commons License