Abstract
A new algorithm is proposed to estimate parameters of autoregressive (AR) processes with output data corrupted by white noise. This algorithm is based on the bias compensated least-squares (BCLS) principle which is introduced by Sagara and Wada for estimation of pulse transfer function models. The BCLS method requires the estimate of the unknown variance of the noise to compensate the biases of the LS estimates. For this end, a variance estimation algorithm is developed to obtain the consistent estimate. This algorithm is joined with the BCLS algorithm to provide consistent estimates of the AR parameters. The algorithm proposed here is a modified version of Sakai and Arase's algorithm which often gives negative values of the variance estimate. Several simulation results indicate that the proposed algorithm is more effective than Sakai and Arase's from statistical and computational points of view.
