Partial Multivariate Errors-in-Variables Model and Its Application in Settlement Monitoring

Aiming at the problem that the coefficient matrix of multivariate errors-in-variables (MEIV) model contains constant columns, the MEIV model is extended to Partial multivariate errors-in-variables (P-MEIV), and the new algorithm of P-MEIV model is proposed based on the principle of Partial errors-in-variables (PEIV) model and indirect adjustment. The algorithm is simple and easy to implement. An example of coordinate transformation is used for verifying, and the results are compared with the existing MEIV model algorithm, which shows the effectiveness of the proposed algorithm. Finally, the P-MEIV algorithm is applied to the multi-point grey model (MGM(1,N)) of settlement monitoring. The results show that the P-MEIV model proposed in this paper can better consider the influence of monitoring point errors, and the estimated results are in good agreement with the actual situation.

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Publication Details

Journal
Numerical Analysis and Applications
Published
2024-09-01
DOI
https://doi.org/10.1134/s1995423924030017
Primary Topic
Statistical and numerical algorithms
Type
article
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article

Partial Multivariate Errors-in-Variables Model and Its Application in Settlement Monitoring

Frank B. Hu, Qisheng Wang
Numerical Analysis and Applications
Statistical and numerical algorithms
article

Partial Multivariate Errors-in-Variables Model and Its Application in Settlement Monitoring

Frank B. Hu, Qisheng Wang
article en

Abstract

Aiming at the problem that the coefficient matrix of multivariate errors-in-variables (MEIV) model contains constant columns, the MEIV model is extended to Partial multivariate errors-in-variables (P-MEIV), and the new algorithm of P-MEIV model is proposed based on the principle of Partial errors-in-variables (PEIV) model and indirect adjustment. The algorithm is simple and easy to implement. An example of coordinate transformation is used for verifying, and the results are compared with the existing MEIV model algorithm, which shows the effectiveness of the proposed algorithm. Finally, the P-MEIV algorithm is applied to the multi-point grey model (MGM(1,N)) of settlement monitoring. The results show that the P-MEIV model proposed in this paper can better consider the influence of monitoring point errors, and the estimated results are in good agreement with the actual situation.

Numerical Analysis and ApplicationsVol. 17(3)
Xiangtan University (CN)
Openalex Percentile: Top 74%
Statistical and numerical algorithms
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Partial Multivariate Errors-in-Variables Model and Its Application in Settlement Monitoring — Frank B. Hu, Qisheng Wang · Numerical Analysis and Applications (2024) | TGRS Research Map | TGRS