1998
DOI: 10.1006/jmva.1998.1755
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Multivariate Stable Densities as Functions of One Dimensional Projections

Abstract: The density of a general d-dimensional stable random vector X is expressed as an integral over the sphere in R d of a function of the parameters of the one dimensional projections of X. These formulas give insight into the form of multivariate stable densities and are useful for numerical calculations. Corollaries give simplified expressions for symmetric stable and the :=1 strictly stable densities, relations among the densities in different dimensions, and values of the densities at the location parameter fo… Show more

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Cited by 31 publications
(19 citation statements)
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“…(38), the case of multivariate symmetric Levy laws is discussed in great details in refs. [34] and [35]. [We don't recapitulate here the well-known results for multivariate Fourier-transforms of Gaussian densities, corresponding to the case of α = 2.]…”
Section: Multivariate Symmetric Lévy Distributionsmentioning
confidence: 99%
“…(38), the case of multivariate symmetric Levy laws is discussed in great details in refs. [34] and [35]. [We don't recapitulate here the well-known results for multivariate Fourier-transforms of Gaussian densities, corresponding to the case of α = 2.]…”
Section: Multivariate Symmetric Lévy Distributionsmentioning
confidence: 99%
“…This condition is equivalent to (1) and can be taken as a definition of stability. More generally, linear combinations of independent stable laws with the same α are stable: if X j ∼ S(α, β j , γ j , δ j ; k) for j = 1, .…”
Section: Example 3 Lévy Distributions X ∼ Lévy(γ δ) If It Has Densitymentioning
confidence: 99%
“…This is a function of the lack of closed form expressions for densities, and the possible complexity of the dependence structures. Byczkowski et al (1993), Abdul-Hamid and Nolan (1998) and Nolan (2007) give expressions for general multivariate stable densities and distribution functions. In the bivariate case, there are some methods of computing densities and estimating, but these are difficult to implement in higher dimensions.…”
Section: Introductionmentioning
confidence: 99%