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dc.contributor.author | Suraci,Stefano Sampaio | es_ES |
dc.contributor.author | Castro de Oliveira, Leonardo | es_ES |
dc.contributor.author | Klein, Ivandro | es_ES |
dc.contributor.author | Rofatto, Vinicius Francisco | es_ES |
dc.contributor.author | Matsuoka, Marcelo Tomio | es_ES |
dc.contributor.author | Baselga Moreno, Sergio | es_ES |
dc.date.accessioned | 2023-11-03T19:02:11Z | |
dc.date.available | 2023-11-03T19:02:11Z | |
dc.date.issued | 2021-06-19 | es_ES |
dc.identifier.issn | 1024-123X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/199213 | |
dc.description.abstract | [EN] Robust estimators are often lacking a closed-form expression for the computation of their residual covariance matrix. In fact, it is also a prerequisite to obtain critical values for normalized residuals. We present an approach based on Monte Carlo simulation to compute the residual covariance matrix and critical values for robust estimators. Although initially designed for robust estimators, the new approach can be extended for other adjustment procedures. In this sense, the proposal was applied to both well-known minimum L1-norm and least squares into three different leveling network geometries. The results show that (1) the covariance matrix of residuals changes along with the estimator; (2) critical values for minimum L1-norm based on a false positive rate cannot be derived from well-known test distributions; (3) in contrast to critical values for extreme normalized residuals in least squares, critical values for minimum L1-norm do not necessarily tend to be higher as network redundancy increases. | es_ES |
dc.description.sponsorship | This work was supported by the Department of Science and Technology of the Brazilian Army. The authors would like to thank the research group "Controle de Qualidade e Inteligencia Computacional em Geodesia" (dgp.cnpq.br/dgp/espelhogrupo/0178611310347329). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Hindawi Limited | es_ES |
dc.relation.ispartof | Mathematical Problems in Engineering | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject.classification | INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA | es_ES |
dc.title | Monte Carlo-Based Covariance Matrix of Residuals and Critical Values in Minimum L1-Norm | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1155/2021/8123493 | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica - Escola Tècnica Superior d'Enginyeria Geodèsica, Cartogràfica i Topogràfica | es_ES |
dc.description.bibliographicCitation | Suraci, SS.; Castro De Oliveira, L.; Klein, I.; Rofatto, VF.; Matsuoka, MT.; Baselga Moreno, S. (2021). Monte Carlo-Based Covariance Matrix of Residuals and Critical Values in Minimum L1-Norm. Mathematical Problems in Engineering. 2021:1-9. https://doi.org/10.1155/2021/8123493 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1155/2021/8123493 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 9 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 2021 | es_ES |
dc.relation.pasarela | S\448252 | es_ES |