14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper WeA2.4

Hong, Mei (Uppsala Univ.), Söderström, Torsten (Uppsala Univ.), Zheng, Wei Xing (Univ. of Western Sydney)

Accuracy Analysis of Bias-Eliminating Least Squares Estimates for Errors-In-Variables Identification

Scheduled for presentation during the Regular Session "Errors in Variables Identification" (WeA2), Wednesday, March 29, 2006, 11:30−11:50, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Errors in Variables Identification


The bias-eliminating least squares (BELS) method is one of the consistent estimators for identifying dynamic errors-in-variables systems. The attraction of the BELS method lies in its good accuracy and its modest computational cost. In this paper, we investigate the accuracy properties of the BELS estimates. It is shown that the estimated system parameters and the estimated noise variances are asymptotically Gaussian distributed. An explicit expression for the normalized covariance matrix of the estimated parameters is derived and supported by some numerical examples.