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ARFIT2 estimates multivariate autoregressive parameters


function [w, MAR, C, sbc, fpe, th] = arfit2(Y, pmin, pmax, selector, no_const)


 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)

  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]'; 

  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

  [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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