tvaar

 TVAAR wrapper around adaptive autoregressive estimator. 
       X = tvaar(signal,p,UC)
       X = tvaar(signal,X)
  
 INPUT:
   X.MOP=[d,p,q]
       d = 1: with mean term; d=0: without mean term
       p:    order of AutoRegressive part 
       q:    order of Moving Average
   X.UC  update coefficient
   X.Mode = [amode,vmode]: choose estimation algorithm    
   X.Z0  covariance of state estimates
   X.z0  initial state vector 
   X.W0  covariance of system noise 
   X.V0  variance of observation noise

 OUTPUT:
   X.Z0  average covariance of state estimates
   X.z0  average state vector 
   X.W0  covariance of system noise 
   X.V0  variance of prediction error
   X.AAR estimated AAR parameters
   X.E   prediction error, residuum, 
   X.PE  time-varying variance of residual process
 
 REFERENCES: 
 [1] Schlögl A.(2000)
   The electroencephalogram and the adaptive autoregressive model: theory and applications
   Shaker Verlag, Aachen, Germany,(ISBN3-8265-7640-3). 
 [2] Schlögl A, Lee FY, Bischof H, Pfurtscheller G
   Characterization of Four-Class Motor Imagery EEG Data for the BCI-Competition 2005.
   Journal of neural engineering 2 (2005) 4, S. L14-L22