OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL

Autores

  • Jun Zhao UFPR
  • Qingming Gui Institute of Science, Information Engineering University

Palavras-chave:

Partial EIV model, Two-step iterated method, Weighted total least-squares, Outlier detection, Data-snooping, Two-dimensional affine transformation

Resumo

The weighed total least square (WTLS) estimate is very sensitive to the outliers in the partial EIV model. A new procedure for detecting outliers based on the data-snooping is presented in this paper. Firstly, a two-step iterated method of computing the WTLS estimates for the partial EIV model based on the standard LS theory is proposed. Secondly, the corresponding w-test statistics are constructed to detect outliers while the observations and coefficient matrix are contaminated with outliers, and a specific algorithm for detecting outliers is suggested. When the variance factor is unknown, it may be estimated by the least median squares (LMS) method. At last, the simulated data and real data about two-dimensional affine transformation are analyzed. The numerical results show that the new test procedure is able to judge that the outliers locate in x component, y component or both components in coordinates while the observations and coefficient matrix are contaminated with outliers.

Biografia do Autor

Jun Zhao, UFPR

The weighed total least square (WTLS) estimate is very sensitive to the outliers in the partial EIV model. A new procedure for detecting outliers based on the data-snooping is presented in this paper. Firstly, a two-step iterated method of computing the WTLS estimates for the partial EIV model based on the standard LS theory is proposed. Secondly, the corresponding w-test statistics are constructed to detect outliers while the observations and coefficient matrix are contaminated with outliers, and a specific algorithm for detecting outliers is suggested. When the variance factor is unknown, it may be estimated by the least median squares (LMS) method. At last, the simulated data and real data about two-dimensional affine transformation are analyzed. The numerical results show that the new test procedure is able to judge that the outliers locate in x component, y component or both components in coordinates while the observations and coefficient matrix are contaminated with outliers

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Publicado

2017-03-27

Como Citar

Zhao, J., & Gui, Q. (2017). OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL. Boletim De Ciências Geodésicas, 23(1). Recuperado de https://revistas.ufpr.br/bcg/article/view/51416

Edição

v. 23 n. 1 (2017)

Seção

Artigos

Licença

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