Generalized Variance Functions for Infinitely Divisible Mixture Distributions
| Autor: | Mahdi Louati, Afif Masmoudi, Farouk Mselmi, Célestin C. Kokonendji |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Pure mathematics
Multivariate statistics Covariance matrix General Mathematics 05 social sciences Function (mathematics) 01 natural sciences Measure (mathematics) Convolution 010104 statistics & probability Exponential family Mixing (mathematics) 0502 economics and business 0101 mathematics 050205 econometrics Mathematics Variance function |
| Zdroj: | Mediterranean Journal of Mathematics. 15 |
| ISSN: | 1660-5454 1660-5446 |
| Popis: | This paper deals with the characterization of a class of infinitely divisible mixture distributions when the mixing parameter is the power of convolution. In framework of natural exponential families, we give the expression of its variance function. Furthermore, we explicit its generalized variance function which is the determinant of the covariance matrix and, then, we determine its associated Levy measure. Some important examples of multivariate mixture of discrete distributions are given. Our examples introduce an infinitely divisible family of multivariate discrete models that are lacking in the literature. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink