A new method of parameter estimation considering the sequence observation error of autoregressive model
Qisheng Wang, Feng Hu
College of Civil Engineering, Xiangtan University, Hunan, China
Keywords: autoregressive model, total least squares, adjustment model, parameter estimation
Abstract
Aiming at the problem that observation errors exist in both the observation vector and the coefficient matrix for an autoregressive model, a new parameter estimation method is proposed. First, the observation vector and coefficient matrix are recombined, which avoids the situation that the same observation value appears in both the observation vector and the coefficient matrix. Then a detailed algorithm is derived based on the principle of total least squares and indirect adjustment. Finally, the effectiveness and feasibility of the proposed method are verified by an analysis of validation and simulation examples and compared with the weighted total least squares and the correlation total least squares.
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