  A stochastic model for intermittent fluctuations in the scrape-off layer of
magnetically confined plasmas has been constructed based on a super-position of
uncorrelated pulses arriving according to a Poisson process. In the most common
applications of the model, the pulse amplitudes are assumed exponentially
distributed, supported by conditional averaging of large-amplitude fluctuations
in experimental measurement data. This basic assumption has two potential
limitations. First, statistical analysis of measurement data using conditional
averaging only reveals the tail of the amplitude distribution to be
exponentially distributed. Second, exponentially distributed amplitudes leads
to a positive definite signal which cannot capture fluctuations in for example
electric potential and radial velocity. Assuming pulse amplitudes which are not
positive definite often make finding a closed form for the probability density
function difficult, even if the characteristic function remains relatively
simple. Thus estimating model parameters requires an approach based on the
characteristic function, not the probability density function. In this
contribution, the effect of changing the amplitude distribution on the moments,
probability density function and characteristic function of the process is
investigated and a parameter estimation method using the empirical distribution
function is presented and tested on synthetically generated data. This proves
valuable for describing intermittent fluctuations in the boundary region of
magnetized plasmas.
