A note on the recursions of multichannel complex subset autoregressions

The recursive algorithm to select the optimum multivariate real subset autoregressive model (AR) [1] is generalized to apply to multichannel complex subset AR\´s. It is initiated by fitting all "forward" and "backward" one-lag AR\´s. The method then allows one to develop successively all complex subset AR\´s of size (the number of lags with nonzero coefficient matrices) from 1 to . Finally, the best subsets of each size with the minimum generalized residual power for that size are compared to any one of three model selection criteria to find the optimum multichannel complex subset AR.