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On the Bivariate Composite Gumbel–Pareto Distribution
by
Badea, Alexandra
, Bolancé, Catalina
, Vernic, Raluca
in
Analysis
/ Automobile insurance
/ bivariate composite (two-spliced) distribution
/ bivariate Pareto of the first kind distribution
/ Contracts
/ Expected values
/ Gumbel’s bivariate exponential distribution
/ maximum likelihood estimation procedure
/ Monte Carlo method
/ Parameter estimation
/ Pareto principle
/ Property and casualty insurance industry
/ risk of loss
2022
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On the Bivariate Composite Gumbel–Pareto Distribution
by
Badea, Alexandra
, Bolancé, Catalina
, Vernic, Raluca
in
Analysis
/ Automobile insurance
/ bivariate composite (two-spliced) distribution
/ bivariate Pareto of the first kind distribution
/ Contracts
/ Expected values
/ Gumbel’s bivariate exponential distribution
/ maximum likelihood estimation procedure
/ Monte Carlo method
/ Parameter estimation
/ Pareto principle
/ Property and casualty insurance industry
/ risk of loss
2022
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Do you wish to request the book?
On the Bivariate Composite Gumbel–Pareto Distribution
by
Badea, Alexandra
, Bolancé, Catalina
, Vernic, Raluca
in
Analysis
/ Automobile insurance
/ bivariate composite (two-spliced) distribution
/ bivariate Pareto of the first kind distribution
/ Contracts
/ Expected values
/ Gumbel’s bivariate exponential distribution
/ maximum likelihood estimation procedure
/ Monte Carlo method
/ Parameter estimation
/ Pareto principle
/ Property and casualty insurance industry
/ risk of loss
2022
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Journal Article
On the Bivariate Composite Gumbel–Pareto Distribution
2022
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Overview
In this paper, we propose a bivariate extension of univariate composite (two-spliced) distributions defined by a bivariate Pareto distribution for values larger than some thresholds and by a bivariate Gumbel distribution on the complementary domain. The purpose of this distribution is to capture the behavior of bivariate data consisting of mainly small and medium values but also of some extreme values. Some properties of the proposed distribution are presented. Further, two estimation procedures are discussed and illustrated on simulated data and on a real data set consisting of a bivariate sample of claims from an auto insurance portfolio. In addition, the risk of loss in this insurance portfolio is estimated by Monte Carlo simulation.
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