OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL
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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Universidade Federal do Paraná
2017
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oai:scielo:S1982-217020170001000012017-04-07OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODELZHAO,JUNGUI,QINGMING Partial EIV model Two-step iterated method Weighted total least-squares Outlier detection Data-snooping Two-dimensional affine transformation 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 outliersinfo:eu-repo/semantics/openAccessUniversidade Federal do ParanáBoletim de Ciências Geodésicas v.23 n.1 20172017-03-01info:eu-repo/semantics/articletext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1982-21702017000100001en10.1590/s1982-21702017000100001 |
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ZHAO,JUN GUI,QINGMING |
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ZHAO,JUN GUI,QINGMING OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL |
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| title |
OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL |
| title_short |
OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL |
| title_full |
OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL |
| title_fullStr |
OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL |
| title_full_unstemmed |
OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL |
| title_sort |
outlier detection in partial errors-in-variables model |
| description |
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 |
| publisher |
Universidade Federal do Paraná |
| publishDate |
2017 |
| url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1982-21702017000100001 |
| work_keys_str_mv |
AT zhaojun outlierdetectioninpartialerrorsinvariablesmodel AT guiqingming outlierdetectioninpartialerrorsinvariablesmodel |
| _version_ |
1756435441537515520 |