Multivariate stable distribution

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multivariate stable
Probability density function
Mv stable.png
Heatmap showing a Multivariate (bivariate) stable distribution with α = 1.1
Parameters \alpha \in (0,2]exponent
\delta \in \mathbb{R}^d - shift/location vector
\Lambda(s) - a spectral finite measure on the sphere
Support u \in \mathbb{R}^d
PDF (no analytic expression)
CDF (no analytic expression)
Variance Infinite when \alpha < 2
CF see text

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.[clarification needed] 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.


Let  \mathbb{S} be the unit sphere in \mathbb R^d : \mathbb{S} = \{u \in \mathbb R^d : |u| = 1\}. 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[1]

\operatorname{E} \exp(u^T X) = \exp \left\{-\int \limits_{s \in \mathbb S}\left\{|u^Ts|^\alpha + i \nu (u^Ts, \alpha) \right\} \, \Lambda(ds) + i u^T\delta\right\}

where 0 < α < 2, and for  y\in\mathbb R

\nu(y,\alpha) =\begin{cases} -\mathbf{sign}(y) \tan(\pi \alpha / 2)|y|^\alpha & \alpha \ne 1, \\
(2/\pi)y \ln |y| & \alpha=1. \end{cases}

This is essentially the result of Feldheim,[2] 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.

Parametrization using projections[edit]

Another way to describe a stable random vector is in terms of projections. For any vector  u , the projection u^TX is univariate \alpha-stable with some skewness \beta(u), scale \gamma(u) and some shift \delta(u). The notation X \sim S(\alpha,\beta(\cdot),\gamma(\cdot),\delta(\cdot)) is used if X is stable with u^TX \sim s(\alpha,\beta(\cdot),\gamma(\cdot),\delta(\cdot)) for every u \in \mathbb R^d. This is called the projection parameterization.

The spectral measure determines the projection parameter functions by:

\gamma(u) =
\int_{s \in \mathbb{S}} |u^Ts|^\alpha \Lambda(ds)

\beta(u) = \int_{s \in \mathbb{S}}|u^Ts|^\alpha \mathbf{sign}(u^Ts)\Lambda(ds)
\delta(u)=\begin{cases}u^T \delta & \alpha \ne 1\\u^T \delta -\int_{s \in \mathbb{S}}\tfrac{\pi}{2} u^Ts \ln|u^Ts|\Lambda(ds)&\alpha=1\end{cases}

Special cases[edit]

There are special cases where the multivariate characteristic function takes a simpler form. Define the characteristic function of a stable marginal as

\omega(y|\alpha,\beta) = 
\begin{cases}|y|^\alpha\left[1-i \beta(\tan  \tfrac{\pi\alpha}{2})\mathbf{sign}(y)\right]& \alpha \ne 1\\
|y|\left[1+i \beta \tfrac{2}{\pi} \mathbf{sign}(y)\ln |y|\right] & \alpha = 1\end{cases}

Isotropic multivariate stable distribution[edit]

The characteristic function is E \exp(i u^T X)=\exp\{-\gamma_0|u|^\alpha+i u^T \delta)\} The spectral measure is continuous and uniform, leading to radial/isotropic symmetry.[3] For the multinormal case \alpha=2, this corresponds to independent components, but so is not the case when \alpha=2. Isotropy is a special case of ellipticity (see the next paragraph) – just take \Sigma to be a multiple of the identity matrix.

Elliptically contoured multivariate stable distribution[edit]

Elliptically contoured m.v. stable distribution is a special symmetric case of the multivariate stable distribution. If X is α-stable and elliptically contoured, then it has joint characteristic function E \exp(i u^T X)=\exp\{-(u^T\Sigma u)^{\alpha/2}+i u^T \delta)\} for some shift vector \delta \in R^d (equal to the mean when it exists) and some positive definite matrix \Sigma (akin to a correlation matrix, although the usual definition of correlation fails to be meaningful). Note the relation to characteristic function of the multivariate normal distribution: E \exp(i u^T X)=\exp\{-(u^T\Sigma u)+i u^T \delta)\} obtained when α = 2.

Independent components[edit]

The marginals are independent with X_j \sim S(\alpha, \beta_j, \gamma_j, \delta_j), then the characteristic function is

E \exp(i u^T X) = \exp\left\{-\sum_{j=1}^m \omega(u_j|\alpha,\beta_j)\gamma_j^\alpha +i u^T \delta)\right\}

Observe that when α = 2 this reduces again to the multivariate normal; note that the iid case and the isotropic case do not coincide when α < 2. Independent components is a special case of discrete spectral measure (see next paragraph), with the spectral measure supported by the standard unit vectors.

Mv indp
Heatmap showing a multivariate (bivariate) independent stable distribution with α = 1

Mv indp2
Heatmap showing a multivariate (bivariate) independent stable distribution with α = 2.


If the spectral measure is discrete with mass \lambda_j at s_j \in \mathbb{S},j=1,\ldots,m the characteristic function is

E \exp(i u^T X)= \exp\left\{-\sum_{j=1}^m \omega(u^Ts_j|\alpha,1)\gamma_j^\alpha +i u^T \delta)\right\}

Linear properties[edit]

if X \sim S(\alpha, \beta(\cdot), \gamma(\cdot), \delta(\cdot))
is d-dim, and A is a m x d matrix, b \in \mathbb{R}^m then AX + b is m dim. \alpha-stable with scale function \gamma(A^T\cdot) , skewness function \beta(A^T\cdot), and location function \delta(A^T\cdot) + b^T\cdot

Inference in the independent component model[edit]

Recently[4] it was shown how to compute inference in closed-form in a linear model (or equivalently a factor analysis model),involving independent component models.

More specifically, let X_i \sim S(\alpha, \beta_{x_i}, \gamma_{x_i}, \delta_{x_i}), i=1,\ldots,n be a set of i.i.d. unobserved univariate drawn from a stable distribution. Given a known linear relation matrix A of size n \times n, the observation Y_i = \sum_{i=1}^n A_{ij}X_j are assumed to be distributed as a convolution of the hidden factors X_i. Y_i = S(\alpha, \beta_{y_i}, \gamma_{y_i},  \delta_{y_i}). The inference task is to compute the most probable X_i, given the linear relation matrix A and the observations Y_i. This task can be computed in closed-form in O(n3).

An application for this construction is multiuser detection with stable, non-Gaussian noise.



  1. ^ J. Nolan, Multivariate stable densities and distribution functions: general and elliptical case, BundesBank Conference, Eltville, Germany, 11 November 2005. See also
  2. ^ Feldheim, E. (1937). Etude de la stabilité des lois de probabilité . Ph. D. thesis, Faculté des Sciences de Paris, Paris, France.
  3. ^ User manual for STABLE 5.1 Matlab version, Robust Analysis Inc.,
  4. ^ D. Bickson and C. Guestrin. Inference in linear models with multivariate heavy-tails. In Neural Information Processing Systems (NIPS) 2010, Vancouver, Canada, Dec. 2010.