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dc.contributor | 31249 | es_ES |
dc.contributor.other | https://orcid.org/0000-0002-7337-8974 | |
dc.coverage.spatial | Global | es_ES |
dc.creator | De la Rosa Vargas, José Ismael | |
dc.creator | Fleury, Gilles | |
dc.date.accessioned | 2020-04-14T19:58:34Z | |
dc.date.available | 2020-04-14T19:58:34Z | |
dc.date.issued | 2006-06 | |
dc.identifier | info:eu-repo/semantics/publishedVersion | es_ES |
dc.identifier.issn | 0018-9456 | es_ES |
dc.identifier.issn | 1557-9662 | es_ES |
dc.identifier.uri | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1654 | |
dc.identifier.uri | https://doi.org/10.48779/98et-sw82 | |
dc.description.abstract | In this paper, a new approach for the statistical characterization of a measurand is presented. A description of how different bootstrap techniques can be applied in practice to estimate successfully a measurand probability density function (pdf) is given. When the direct observation of a quantity of interest is practically impossible such as in nondestructive testing, it is necessary to estimate such quantity, which is also called measurand. The statistical characterization of any estimator is important, because all the uncertainty features can be accessible to qualify such estimator. On the other hand, most of the time, the large-scale repetition of an experiment is not economically feasible, so that the Monte Carlo methods cannot be used directly for uncertainty characterization. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | IEEE Instrumentation and Measurement Society | es_ES |
dc.relation | DOI: 10.1109/TIM.2006.873779 | es_ES |
dc.relation.uri | generalPublic | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.source | Transaction on Instrumentation and Measurement, Vol. 55, No. 3, junio 2006, pp. 820-827 | es_ES |
dc.subject.classification | INGENIERIA Y TECNOLOGIA [7] | es_ES |
dc.subject.other | Bootstrap | es_ES |
dc.subject.other | indirect measurement | es_ES |
dc.subject.other | Monte Carlo simulation | es_ES |
dc.subject.other | nonlinear regression | es_ES |
dc.title | Bootstrap Methods for a Measurement Estimation Problem | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
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