How to obtain the Rosenblatt distribution?

A finite-variance stationary time displays long-range dependence or long memory if its covariance function decays to zero like a power. The decay should be slow enough so that the sum of the covariances diverges. This happens, for example, if the corresponding spectral density blows up at zero frequency like a power function. These types of time series have been widely studied. Normalized sums of such time series can converge to a Gaussian process, typically fractional Brownian motion, but also to non-Gaussian processes, which can be represented by multiple integrals. But unfortunately, even the marginal distributions of these non-Gaussian processes are not known explicitly. The simplest non-Gaussian member of this family is the Rosenblatt process. It is represented by a double integral. We shall study its marginal distributions and describe a way to obtain them numerically. This is joint work with Mark Veillette.