Simulations of full multivariate Tweedie with flexible dependence structure

Johann Cuenin (), Bent Jørgensen () and Célestin C. Kokonendji ()
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Johann Cuenin: Université de Franche-Comté, UFR Sciences et Techniques
Bent Jørgensen: University of Southern Denmark
Célestin C. Kokonendji: Université de Franche-Comté, UFR Sciences et Techniques

Computational Statistics, 2016, vol. 31, issue 4, No 12, 1477-1492

Abstract: Abstract We employ a variables-in-common method for constructing multivariate Tweedie distributions, based on linear combinations of independent univariate Tweedie variables. The method lies on the convolution and scaling properties of the Tweedie laws, using the cumulant generating function for characterization of the distributions and correlation structure. The routine allows the equivalence between independence and zero correlation and gives a parametrization through given values of the mean vector and dispersion matrix, similarly to the Gaussian vector. Our approach leads to a matrix representation of multivariate Tweedie models, which permits the simulations of many known distributions, including Gaussian, Poisson, non-central gamma, gamma, and inverse Gaussian, both positively or negatively correlated.

Keywords: Cumulant generating function; Correlation; Multivariate exponential dispersion model; $$\alpha $$ α -Stable distribution (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s00180-015-0617-3

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