On the asymptotic accuracy of pseudo-linear regression algorithms - STOICA, P; SODERSTROM, T; AHLEN, A; SOLBRAND, G (International journal of control, 1984)
The accuracy properties of a general pseudo-linear regression (PLR) method are examined. Both off-line and on-line algorithms are considered. Assuming the parameter estimates converge they will under weak conditions be asymptotically gaussian distributed. Expressions for the corresponding covariance matrices are given. It is shown that the asymptotic covariance matrix of the off-line PLR algorithm is bounded from above by the matrix corresponding to the on-line PLR algorithm and from below by that corresponding to the prediction error method. Some simple numerical illustrations of the theoretical results are also included.