  The sub-Gaussian stable distribution is a heavy-tailed elliptically contoured
law which has interesting applications in signal processing and financial
mathematics. This work addresses the problem of feasible estimation of
distributions. We present a method based on application of the method of
moments to the empirical characteristic function. Further, we show almost sure
convergence of our estimators, discover their limiting distribution and
demonstrate their finite-sample performance.
