OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Boletim de Ciências Geodésicas |
Texto Completo: | https://revistas.ufpr.br/bcg/article/view/51416 |
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. |
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Boletim de Ciências Geodésicas |
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OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODELGeociências; GeodésiaPartial EIV model; Two-step iterated method; Weighted total least-squares; Outlier detection; Data-snooping; Two-dimensional affine transformationThe 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.Boletim de Ciências GeodésicasBulletin of Geodetic SciencesNational Natural Science Foundation of ChinaState Key Laboratory of Geodesy and Earth’s DynamicsZhao, JunGui, Qingming2017-03-27info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://revistas.ufpr.br/bcg/article/view/51416Boletim de Ciências Geodésicas; Vol 23, No 1 (2017)Bulletin of Geodetic Sciences; Vol 23, No 1 (2017)1982-21701413-4853reponame:Boletim de Ciências Geodésicasinstname:Universidade Federal do Paraná (UFPR)instacron:UFPRporhttps://revistas.ufpr.br/bcg/article/view/51416/31874Copyright (c) 2017 Jun Zhao, Qingming Guihttp://creativecommons.org/licenses/by-nc/4.0info:eu-repo/semantics/openAccess2018-09-13T19:47:39Zoai:revistas.ufpr.br:article/51416Revistahttps://revistas.ufpr.br/bcgPUBhttps://revistas.ufpr.br/bcg/oaiqdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br1982-21701413-4853opendoar:2018-09-13T19:47:39Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR)false |
dc.title.none.fl_str_mv |
OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL |
title |
OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL |
spellingShingle |
OUTLIER DETECTION IN PARTIAL ERRORS-IN-VARIABLES MODEL Zhao, Jun Geociências; Geodésia Partial EIV model; Two-step iterated method; Weighted total least-squares; Outlier detection; Data-snooping; Two-dimensional affine transformation |
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 |
author |
Zhao, Jun |
author_facet |
Zhao, Jun Gui, Qingming |
author_role |
author |
author2 |
Gui, Qingming |
author2_role |
author |
dc.contributor.none.fl_str_mv |
National Natural Science Foundation of China State Key Laboratory of Geodesy and Earth’s Dynamics |
dc.contributor.author.fl_str_mv |
Zhao, Jun Gui, Qingming |
dc.subject.por.fl_str_mv |
Geociências; Geodésia Partial EIV model; Two-step iterated method; Weighted total least-squares; Outlier detection; Data-snooping; Two-dimensional affine transformation |
topic |
Geociências; Geodésia Partial EIV model; Two-step iterated method; Weighted total least-squares; Outlier detection; Data-snooping; Two-dimensional affine transformation |
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. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-03-27 |
dc.type.none.fl_str_mv |
|
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/51416 |
url |
https://revistas.ufpr.br/bcg/article/view/51416 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://revistas.ufpr.br/bcg/article/view/51416/31874 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2017 Jun Zhao, Qingming Gui http://creativecommons.org/licenses/by-nc/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2017 Jun Zhao, Qingming Gui http://creativecommons.org/licenses/by-nc/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
publisher.none.fl_str_mv |
Boletim de Ciências Geodésicas Bulletin of Geodetic Sciences |
dc.source.none.fl_str_mv |
Boletim de Ciências Geodésicas; Vol 23, No 1 (2017) Bulletin of Geodetic Sciences; Vol 23, No 1 (2017) 1982-2170 1413-4853 reponame:Boletim de Ciências Geodésicas instname:Universidade Federal do Paraná (UFPR) instacron:UFPR |
instname_str |
Universidade Federal do Paraná (UFPR) |
instacron_str |
UFPR |
institution |
UFPR |
reponame_str |
Boletim de Ciências Geodésicas |
collection |
Boletim de Ciências Geodésicas |
repository.name.fl_str_mv |
Boletim de Ciências Geodésicas - Universidade Federal do Paraná (UFPR) |
repository.mail.fl_str_mv |
qdalmolin@ufpr.br|| danielsantos@ufpr.br||qdalmolin@ufpr.br|| danielsantos@ufpr.br |
_version_ |
1799771719378927616 |