  We investigate methods to best estimate the normalisation of the mass density
fluctuation power spectrum (sigma_8) using peculiar velocity data from a survey
like the Six degree Field Galaxy Velocity Survey (6dFGSv). We focus on two
potential problems (i) biases from nonlinear growth of structure and (ii) the
large number of velocities in the survey. Simulations of LambdaCDM-like models
are used to test the methods. We calculate the likelihood from a full
covariance matrix of velocities averaged in grid cells. This simultaneously
reduces the number of data points and smooths out nonlinearities which tend to
dominate on small scales. We show how the averaging can be taken into account
in the predictions in a practical way, and show the effect of the choice of
cell size. We find that a cell size can be chosen that significantly reduces
the nonlinearities without significantly increasing the error bars on
cosmological parameters. We compare our results with those from a principal
components analysis following Watkins et al (2002) and Feldman et al (2003) to
select a set of optimal moments constructed from linear combinations of the
peculiar velocities that are least sensitive to the nonlinear scales. We
conclude that averaging in grid cells performs equally well. We find that for a
survey such as 6dFGSv we can estimate sigma_8 with less than 3% bias from
nonlinearities. The expected error on sigma_8 after marginalising over Omega_m
is approximately 16 percent.
