OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL

Authors

  • Jun Zhao Xi’an Technical Division of Surveying and Mapping; State Key Laboratory of Geodesy and Earth’s Dynamics; School of Surveying mapping, Information Engineering University.
  • Qingming Gui Institute of Science, Information Engineering University

Keywords:

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

Abstract

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.

Author Biography

Jun Zhao, Xi’an Technical Division of Surveying and Mapping; State Key Laboratory of Geodesy and Earth’s Dynamics; School of Surveying mapping, Information Engineering University.

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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Published

2017-03-27

How to Cite

Zhao, J., & Gui, Q. (2017). OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL. Bulletin of Geodetic Sciences, 23(1). Retrieved from https://revistas.ufpr.br/bcg/article/view/51416

Issue

Vol. 23 No. 1 (2017)

Section

Article

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