ARFIT2 estimates multivariate autoregressive parameters of the MVAR process Y Y(t,:)' = w' + A1*Y(t-1,:)' + ... + Ap*Y(t-p,:)' + x(t,:)' ARFIT2 uses the Nutall-Strand method (multivariate Burg algorithm) which provides better estimates the ARFIT [1], and uses the same arguments. Moreover, ARFIT2 is faster and can deal with missing values encoded as NaNs. [w, A, C, sbc, fpe] = arfit2(v, pmin, pmax, selector, no_const) INPUT: v data - each channel in a column pmin, pmax minimum and maximum model order selector 'fpe' or 'sbc' [default] no_const 'zero' indicates no bias/offset need to be estimated in this case is w = [0, 0, ..., 0]'; OUTPUT: w mean of innovation noise A [A1,A2,...,Ap] MVAR estimates C covariance matrix of innovation noise sbc, fpe criteria for model order selection see also: ARFIT, MVAR REFERENCES: [1] A. Schloegl, 2006, Comparison of Multivariate Autoregressive Estimators. Signal processing, p. 2426-9. [2] T. Schneider and A. Neumaier, 2001. Algorithm 808: ARFIT-a Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models. ACM-Transactions on Mathematical Software. 27, (Mar.), 58-65.
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