Estimating Adaptive AutoRegressive-Moving-Average-and-mean model (includes mean term) ~~ This function is obsolete and is replaced by AMARMA [z,E,REV,ESU,V,Z,SPUR] = aarmam(y, mode, MOP, UC, z0, Z0, V0, W); Estimates AAR parameters with Kalman filter algorithm y(t) = sum_i(a_i(t)*y(t-i)) + m(t) + e(t) + sum_i(b_i(t)*e(t-i)) State space model z(t) = G*z(t-1) + w(t) w(t)=N(0,W) y(t) = H*z(t) + v(t) v(t)=N(0,V) G = I, z = [m(t),a_1(t-1),..,a_p(t-p),b_1(t-1),...,b_q(t-q)]; H = [1,y(t-1),..,y(t-p),e(t-1),...,e(t-q)]; W = E{(z(t)-G*z(t-1))*(z(t)-G*z(t-1))'} V = E{(y(t)-H*z(t-1))*(y(t)-H*z(t-1))'} Input: y Signal (AR-Process) Mode determines the type of algorithm MOP Model order [m,p,q], default [0,10,0] m=1 includes the mean term, m=0 does not. p and q must be positive integers it is recommended to set q=0. UC Update Coefficient, default 0 z0 Initial state vector Z0 Initial Covariance matrix Output: z AR-Parameter E error process (Adaptively filtered process) REV relative error variance MSE/MSY REFERENCE(S): [1] A. Schloegl (2000), The electroencephalogram and the adaptive autoregressive model: theory and applications. ISBN 3-8265-7640-3 Shaker Verlag, Aachen, Germany. More references can be found at http://www.dpmi.tu-graz.ac.at/~schloegl/publications/

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