  Equalisation Maximisation (EqM) is an algorithm for estimating parameters in
auto-regressive (AR) models where some fraction of the data is missing. It has
previously been shown that the EqM algorithm is a competitive alternative to
expectation maximisation, estimating models with equal predictive capability at
a lower computational cost.
  The EqM algorithm has previously been motivated as a heuristic. In this
paper, we instead show that EqM can be viewed as an approximation of a proximal
point algorithm. We also derive the method for the entire class of Gaussian
models and exemplify its use for estimation of ARMA models with missing data.
The resulting method is evaluated in numerical simulations, resulting in
similar results as for the AR processes.
