# name
On the bivariate Sarmanov distribution and copula. An application on insurance data using truncated marginal distributions

# metadata.dc.description.abstract[].value
The Sarmanov family of distributions can provide a good model for bivariate random variables and it is used to model dependency in a multivariate setting with given marginals. In this paper, we focus our attention on the bivariate Sarmanov distribution and copula with different truncated extreme value marginal distributions. We compare a global estimation method based on maximizing the full log-likelihood function with the estimation based on maximizing the pseudolog- likelihood function for copula (or partial estimation). Our aim is to estimate two statistics that can be used to evaluate the risk of the sum exceeding a given value. Numerical results using a real data set from the motor insurance sector are presented.

# metadata.dc.relation.ispartof[].value
Sort (Statistics and Operations Research Transactions), 2015, vol. 39, num. 2, p. 1-22

# metadata.dc.rights[].value
cc-by-nc-nd (c) Bahraoui, Zuhair et al., 2015

# metadata.dc.source[].value
Articles publicats en revistes  (Econometria, Estadística i Economia Aplicada)

# metadata.dc.subject.classification[].value
Teoria de distribucions (Anàlisi funcional)

# metadata.dc.subject.other[].value
Theory of distributions (Functional analysis)

# metadata.dc.title[].value
On the bivariate Sarmanov distribution and copula. An application on insurance data using truncated marginal distributions