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Multivariate stable distribution : ウィキペディア英語版
Multivariate stable distribution

The multivariate stable distribution is a multivariate probability distribution that is a multivariate generalisation of the univariate stable distribution. The multivariate stable distribution defines linear relations between stable distribution marginals. In the same way as for the univariate case, the distribution is defined in terms of its characteristic function.
The multivariate stable distribution can also be thought as an extension of the multivariate normal distribution. It has parameter, ''α'', which is defined over the range 0 < ''α'' ≤ 2, and where the case ''α'' = 2 is equivalent to the multivariate normal distribution. It has an additional skew parameter that allows for non-symmetric distributions, where the multivariate normal distribution is symmetric.
== Definition ==
Let \mathbb be the unit sphere in \mathbb R^d : \mathbb = \. A random vector, X , has a multivariate stable distribution - denoted as X \sim S(\alpha, \Lambda, \delta) -, if the joint characteristic function of X is〔J. Nolan, Multivariate stable densities and distribution functions: general and elliptical case, BundesBank Conference, Eltville, Germany, 11 November 2005. See also http://academic2.american.edu/~jpnolan/stable/stable.html〕
: \operatorname \exp(u^T X) = \exp \left\ \, \Lambda(ds) + i u^T\delta\right\}
where 0 < ''α'' < 2, and for y\in\mathbb R
:\nu(y,\alpha) =\begin -\mathbf(y) \tan(\pi \alpha / 2)|y|^\alpha & \alpha \ne 1, \\
(2/\pi)y \ln |y| & \alpha=1. \end
This is essentially the result of Feldheim,〔Feldheim, E. (1937). Etude de la stabilité des lois de probabilité . Ph. D. thesis, Faculté des Sciences de Paris, Paris, France.〕 that any stable random vector can be characterized by a spectral measure \Lambda (a finite measure on \mathbb S) and a shift vector \delta \in \mathbb R^d.

抄文引用元・出典: フリー百科事典『 ウィキペディア(Wikipedia)
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