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Abstract <p>Aiming at the problem that the observation errors exist in both the observation vector and the coefficient matrix for 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, the 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 the analysis of the validation and simulation examples, and compared with the weighted total least squares and the correlation total least squares.</p>

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A New Method of Parameter Estimation Considering the Sequence Observation Error of Autoregressive Model

  • 期刊论文

  • 发表日期：

    2025年6月1日

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  • Q. Wang,
  • F. Hu

摘要

Abstract

Aiming at the problem that the observation errors exist in both the observation vector and the coefficient matrix for 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, the 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 the analysis of the validation and simulation examples, and compared with the weighted total least squares and the correlation total least squares.