|
|
Bivariate Poisson distribution
Smolárová, Tereza ; Hudecová, Šárka (advisor) ; Hlubinka, Daniel (referee)
In the present thesis we deal with the bivariate Poisson distribution. A trivariate reduction method is used to define the bivariate Poisson dis- tribution. The theoretical characteristics of distribution, which this thesis deals with are the marginal distributions, covariance, a correlation coeffi- cient and the conditional distributions. A method of moments and a method of maximum likelihood are used to construct the point estimations of the parameters. Further, we focus on testing the goodness of fit by the index of dispersion test. A transforms of the sample correlation test is used to test the independence. Both methods for estimating the parameters and the statistic tests are applied to real data from the insurance field. 1
|
|
|
Multivariate claim numbers models
Zušťáková, Lucie ; Mazurová, Lucie (advisor) ; Cipra, Tomáš (referee)
Multidimensional frequency models can be used for modeling number of claims from different branches which are somehow dependent on each other. As in the one-dimensional case Poisson distribution and negative binomial distribution are primarily used for modeling multidimensional claim counts data, only they are extended to higher dimensions. The generalization of multi- dimensional distributions is often done using so-called shock variables, where one random variable is included in all dimensions of a random vector which models claim counts. The more comprehensive approach to modeling dependence uses copulas. Comparison of these models is done on a simulated data of number of claims from two different car insurance guarantees.
|
|
|
Bivariate Poisson distribution
Smolárová, Tereza ; Hudecová, Šárka (advisor) ; Hlubinka, Daniel (referee)
In the present thesis we deal with the bivariate Poisson distribution. A trivariate reduction method is used to define the bivariate Poisson dis- tribution. The theoretical characteristics of distribution, which this thesis deals with are the marginal distributions, covariance, a correlation coeffi- cient and the conditional distributions. A method of moments and a method of maximum likelihood are used to construct the point estimations of the parameters. Further, we focus on testing the goodness of fit by the index of dispersion test. A transforms of the sample correlation test is used to test the independence. Both methods for estimating the parameters and the statistic tests are applied to real data from the insurance field. 1
|
guest ::
